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OSSL NeoSpectra soil NIR and MIR

ossl · NIR

OSSL NeoSpectra soil NIR and MIR. Origin database composed from 2 standardized v2.0 leaves: 2 spectral source(s), 23 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ossl
Original source available without a Dataverse token. The catalog uses the private tier for restricted or uncleared redistribution rights. You can obtain original resources from the source; their license still applies. Raw retrieval and automatic assembly are separate; use the loading example below.
2,106
samples
2,302
wavelengths
2
sources
23
targets
30
metadata
NIR
family

Provenance & citation

Where the original data and publications come from. Follow these links to obtain the source and cite its authors. Reading guide

ContributorOSSL_NIRS
Conversion statuspartial
Origin · url [open]https://storage.googleapis.com/soilspec4gg-public/neospectra_nir_v1.2.csv.gz
Origin · url [open]https://storage.googleapis.com/soilspec4gg-public/neospectra_soilsite_v1.2.csv.gz
Origin · url [open]https://storage.googleapis.com/soilspec4gg-public/neospectra_soillab_v1.2.csv.gz

Governance & integrity

Source license, redistribution conditions, content version and identity hashes. Redistribution tier and original-source availability are separate. Reading guide

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking; private use only.
Access policyManual download / private-use-only per source.
RedistributionRecovered from local initial-source exports; rights not cleared for redistribution.
Content version1.1.0
Schema / protocol2.0
Content hash5c7f05f7eb2f0b46…
Processing hash2db467dcbfe09000…
Metadata hashde5275dac627724e…

Dataset property explorer

Start with the profile axes to locate unusual variation, then inspect the measured values and evidence below. Scores are descriptive heuristics. Reading guide

Mean available profile score0.61
Highest axisLocal artifacts · 1.00
Diagnostics10
Sources profiled2

How to read the profile

The center is 0 and the outer ring is 1. Larger values indicate stronger anomaly or heterogeneity signals on that axis. Compare individual axes using the table; this is not an overall quality grade. Missing axes are left blank, and partial profiles have no filled polygon.

OSSL NeoSpectra soil NIR and MIR property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureOSSL NeoSpectra soil NIR and MIR — integrity: 0.00OSSL NeoSpectra soil NIR and MIR — noise: 0.00OSSL NeoSpectra soil NIR and MIR — artefacts: 1.00OSSL NeoSpectra soil NIR and MIR — baseline: 0.65OSSL NeoSpectra soil NIR and MIR — PCA outliers: 0.74OSSL NeoSpectra soil NIR and MIR — reference: 1.00OSSL NeoSpectra soil NIR and MIR — structure: 0.85OSSL NeoSpectra…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Dimensionless scores from 0 to 1 summarize each property; larger values indicate stronger signals. Unavailable axes are not zero.

Integrity
?Combines missing cells, zero columns and infinite values. Higher values call for checking data completeness.
0.00
Noise
?Combines roughness and its signal-to-roughness proxy. Real narrow bands may also contribute.
0.00
Local artifacts
?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause.
1.00
Baseline/shape
?Summarizes baseline slope and excess edge variation relative to signal scale.
0.65
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
0.74
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
1.00
Repeatability
?Dispersion among observations sharing an ID. Interpretation depends on the documented meaning of that ID.
unavailable
Multi-regime structure
?Variation in local density in PCA space. May reflect legitimate batches or subpopulations.
0.85

Possible explanations to investigate

Bars rank heuristic signatures from 0 to 1. They do not estimate the probability of a physical fault. On narrow screens, scroll the chart horizontally. Full labels, scores, evidence coverage, possible causes and required external checks are in the table below.

Diagnostic hypotheses00.250.50.751hypothesis scoreSignature compatible with a spectral spliceSignature compatible with a spectral splice: 0.840.84Signature compatible with a population shiftSignature compatible with a population shift: 0.740.74Offset compatible with a different white referenceOffset compatible with a different white reference: 0.730.73Multi-regime datasetMulti-regime dataset: 0.720.72Signature compatible with a different backgroundSignature compatible with a different background: 0.660.66Signature compatible with resamplingSignature compatible with resampling: 0.640.64Signature compatible with a change in geometrySignature compatible with a change in geometry: 0.630.63Signature compatible with partial coverageSignature compatible with partial coverage: 0.550.55

Heuristic signature scores are not calibrated probabilities of physical causes: 0.78 does not mean a 78% causal probability. Missing evidence remains unavailable. RMS/SAM describe dispersion around the dataset mean, without an independent external reference.

Diagnostic hypotheses and supporting evidence
DiagnosticScoreHeuristic strengthEvidence coverageSignalsPossible causeRequired external checks
Signature compatible with a spectral spliceX_mir0.84strong100 %Spike rate 1.00, Jump rate 1.00, RMS/SAM to mean spectrum 1.00Break at detector junctions, local calibration or different probe.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a population shiftX_nir0.74strong100 %Mahalanobis / T2 1.00, RMS/SAM to mean spectrum 1.00, PCA structure 1.00Different but physically plausible variety, species, batch or condition.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Offset compatible with a different white referenceX_mir0.73strong100 %Baseline/mean/area 1.00, RMS/SAM to mean spectrum 1.00, local artifacts 1.00Systematic discrepancy between campaigns, instruments or white reference.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Multi-regime datasetX_nir0.72strong100 %PCA structure 1.00, RMS/SAM to mean spectrum 1.00, Mahalanobis / T2 1.00Mixture of campaigns, operators, batches, setups or spectral subpopulations.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a different backgroundX_mir0.66medium100 %Baseline/mean/area 1.00, RMS/SAM to mean spectrum 1.00, Mahalanobis / T2 0.49Systematic effect of support, white/black, transflectance or measurement environment.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with resamplingX_nir0.64medium100 %Spike rate 1.00, High S/N roughness proxy 1.00, Low roughness sigma proxy 1.00Digital artifacts or incorrect spectral processing.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a change in geometryX_nir0.63medium100 %Mahalanobis / T2 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.86Changing illumination, collection, angle or probe-sample distance.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with partial coverageX_nir0.55medium100 %Mahalanobis / T2 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.86Partial spot coverage; contribution from the background or support.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.

Spectral sources

Each block is a separate instrument or spectral source. Read its signal summary before using PCA to explore variation between observations. Reading guide

Source coverage
SourceObservationsSample units
recovered_spectra1,9761,976
recovered_spectra8,1512,106

1,976 sample units are measured by every source; 130 by a single source. Sources are joined by declared sample identity, never by row position.

recovered_spectra

X_mir · NIR · unknown

Read the signal and variation summaries for this source. Axis unit: none; signal representation: not recorded.

Spectral signal summary

The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.

recovered_spectra spectra0.00.51.01.52.001,0002,0003,0004,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / none600none — median 1.545 (q25–q75 1.445–1.651)624none — median 1.601 (q25–q75 1.48–1.721)648none — median 1.563 (q25–q75 1.436–1.696)674none — median 1.529 (q25–q75 1.395–1.665)698none — median 1.674 (q25–q75 1.585–1.772)722none — median 1.479 (q25–q75 1.363–1.602)746none — median 1.435 (q25–q75 1.318–1.557)772none — median 1.425 (q25–q75 1.33–1.534)796none — median 1.466 (q25–q75 1.366–1.571)820none — median 1.613 (q25–q75 1.53–1.7)844none — median 1.369 (q25–q75 1.273–1.473)870none — median 1.291 (q25–q75 1.188–1.402)894none — median 1.269 (q25–q75 1.162–1.378)918none — median 1.307 (q25–q75 1.191–1.419)942none — median 1.275 (q25–q75 1.168–1.384)966none — median 1.257 (q25–q75 1.156–1.357)992none — median 1.245 (q25–q75 1.15–1.337)1,016none — median 1.209 (q25–q75 1.117–1.302)1,040none — median 1.148 (q25–q75 1.055–1.237)1,064none — median 1.033 (q25–q75 0.9241–1.146)1,090none — median 0.9575 (q25–q75 0.836–1.093)1,114none — median 0.9917 (q25–q75 0.8662–1.133)1,138none — median 0.9962 (q25–q75 0.8712–1.134)1,162none — median 1.074 (q25–q75 0.9623–1.201)1,188none — median 0.9865 (q25–q75 0.8695–1.121)1,212none — median 0.9359 (q25–q75 0.8196–1.068)1,236none — median 1.073 (q25–q75 0.9642–1.21)1,260none — median 1.343 (q25–q75 1.239–1.447)1,284none — median 1.518 (q25–q75 1.429–1.604)1,310none — median 1.593 (q25–q75 1.487–1.678)1,334none — median 1.611 (q25–q75 1.497–1.711)1,358none — median 1.605 (q25–q75 1.487–1.712)1,382none — median 1.582 (q25–q75 1.471–1.684)1,408none — median 1.545 (q25–q75 1.451–1.637)1,432none — median 1.514 (q25–q75 1.423–1.597)1,456none — median 1.453 (q25–q75 1.365–1.54)1,480none — median 1.4 (q25–q75 1.321–1.496)1,506none — median 1.363 (q25–q75 1.28–1.447)1,530none — median 1.369 (q25–q75 1.279–1.455)1,554none — median 1.3 (q25–q75 1.208–1.392)1,578none — median 1.358 (q25–q75 1.27–1.448)1,602none — median 1.462 (q25–q75 1.377–1.551)1,628none — median 1.489 (q25–q75 1.396–1.595)1,652none — median 1.432 (q25–q75 1.326–1.548)1,676none — median 1.377 (q25–q75 1.282–1.479)1,700none — median 1.23 (q25–q75 1.15–1.318)1,726none — median 1.084 (q25–q75 1.006–1.167)1,750none — median 0.9807 (q25–q75 0.9161–1.06)1,774none — median 0.9996 (q25–q75 0.937–1.078)1,798none — median 1.011 (q25–q75 0.9334–1.101)1,824none — median 0.9023 (q25–q75 0.8361–0.9797)1,848none — median 0.9123 (q25–q75 0.8412–0.993)1,872none — median 1.03 (q25–q75 0.9405–1.127)1,896none — median 0.9568 (q25–q75 0.8756–1.052)1,920none — median 0.807 (q25–q75 0.7374–0.8856)1,946none — median 0.7914 (q25–q75 0.7198–0.8741)1,970none — median 0.8012 (q25–q75 0.7252–0.8877)1,994none — median 0.799 (q25–q75 0.724–0.8893)2,018none — median 0.7231 (q25–q75 0.6538–0.8062)2,044none — median 0.6213 (q25–q75 0.5617–0.7026)2,068none — median 0.5615 (q25–q75 0.5006–0.6496)2,092none — median 0.5457 (q25–q75 0.4807–0.6324)2,116none — median 0.5345 (q25–q75 0.4679–0.6207)2,142none — median 0.5307 (q25–q75 0.4681–0.6193)2,166none — median 0.5091 (q25–q75 0.4427–0.5959)2,190none — median 0.4971 (q25–q75 0.4304–0.5838)2,214none — median 0.4997 (q25–q75 0.4347–0.5863)2,238none — median 0.5041 (q25–q75 0.4424–0.5901)2,264none — median 0.486 (q25–q75 0.4213–0.5701)2,288none — median 0.4676 (q25–q75 0.4001–0.553)2,312none — median 0.4488 (q25–q75 0.379–0.533)2,336none — median 0.4154 (q25–q75 0.3458–0.5013)2,362none — median 0.3739 (q25–q75 0.2945–0.4604)2,386none — median 0.4407 (q25–q75 0.3701–0.5253)2,410none — median 0.4364 (q25–q75 0.3645–0.5219)2,434none — median 0.433 (q25–q75 0.3608–0.5207)2,458none — median 0.4324 (q25–q75 0.3594–0.5206)2,484none — median 0.4404 (q25–q75 0.3657–0.5292)2,508none — median 0.4539 (q25–q75 0.3762–0.5544)2,532none — median 0.4526 (q25–q75 0.3742–0.5528)2,556none — median 0.4483 (q25–q75 0.3709–0.54)2,582none — median 0.4493 (q25–q75 0.3733–0.541)2,606none — median 0.4505 (q25–q75 0.3739–0.5417)2,630none — median 0.4526 (q25–q75 0.3755–0.5446)2,654none — median 0.4574 (q25–q75 0.3786–0.5482)2,680none — median 0.4667 (q25–q75 0.3879–0.5597)2,704none — median 0.4769 (q25–q75 0.3958–0.5707)2,728none — median 0.4884 (q25–q75 0.404–0.5834)2,752none — median 0.5005 (q25–q75 0.4152–0.5976)2,776none — median 0.515 (q25–q75 0.4269–0.6128)2,802none — median 0.5346 (q25–q75 0.4449–0.6314)2,826none — median 0.5578 (q25–q75 0.4636–0.6565)2,850none — median 0.6067 (q25–q75 0.5007–0.7096)2,874none — median 0.6183 (q25–q75 0.5152–0.7243)2,900none — median 0.6421 (q25–q75 0.5335–0.7482)2,924none — median 0.6778 (q25–q75 0.5612–0.7871)2,948none — median 0.6801 (q25–q75 0.5655–0.7909)2,972none — median 0.6891 (q25–q75 0.5753–0.7971)2,998none — median 0.6983 (q25–q75 0.5806–0.8075)3,022none — median 0.7177 (q25–q75 0.5967–0.8311)3,046none — median 0.7396 (q25–q75 0.6167–0.8571)3,070none — median 0.7663 (q25–q75 0.6389–0.8895)3,094none — median 0.793 (q25–q75 0.6575–0.9196)3,120none — median 0.8208 (q25–q75 0.6801–0.9511)3,144none — median 0.8515 (q25–q75 0.7091–0.9875)3,168none — median 0.8795 (q25–q75 0.7348–1.022)3,192none — median 0.9082 (q25–q75 0.7573–1.056)3,218none — median 0.9341 (q25–q75 0.7792–1.087)3,242none — median 0.9529 (q25–q75 0.7949–1.108)3,266none — median 0.9667 (q25–q75 0.8039–1.121)3,290none — median 0.9767 (q25–q75 0.8093–1.132)3,316none — median 0.9903 (q25–q75 0.821–1.147)3,340none — median 1.001 (q25–q75 0.8284–1.163)3,364none — median 1.014 (q25–q75 0.8393–1.179)3,388none — median 1.021 (q25–q75 0.8454–1.192)3,412none — median 1.02 (q25–q75 0.8465–1.193)3,438none — median 1.016 (q25–q75 0.8446–1.192)3,462none — median 1.003 (q25–q75 0.8361–1.179)3,486none — median 0.9872 (q25–q75 0.8237–1.158)3,510none — median 0.9727 (q25–q75 0.8125–1.143)3,536none — median 0.9643 (q25–q75 0.8036–1.134)3,560none — median 0.9628 (q25–q75 0.8057–1.132)3,584none — median 0.97 (q25–q75 0.8126–1.145)3,608none — median 1.006 (q25–q75 0.8461–1.179)3,634none — median 1.02 (q25–q75 0.8554–1.188)3,658none — median 0.8928 (q25–q75 0.7453–1.052)3,682none — median 0.7168 (q25–q75 0.5918–0.8586)3,706none — median 0.6049 (q25–q75 0.4956–0.7505)3,730none — median 0.367 (q25–q75 0.2893–0.4547)3,756none — median 0.3056 (q25–q75 0.2342–0.3874)3,780none — median 0.2835 (q25–q75 0.2125–0.3649)3,804none — median 0.267 (q25–q75 0.199–0.3467)3,828none — median 0.2616 (q25–q75 0.193–0.3433)3,854none — median 0.2452 (q25–q75 0.1786–0.318)3,878none — median 0.2518 (q25–q75 0.183–0.3314)3,902none — median 0.245 (q25–q75 0.1767–0.3226)3,926none — median 0.2477 (q25–q75 0.179–0.3287)3,952none — median 0.2457 (q25–q75 0.1776–0.3267)3,976none — median 0.2439 (q25–q75 0.1755–0.3253)4,000none — median 0.2403 (q25–q75 0.1725–0.3225)

Sampling

How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide

Wavelengths
?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis.
1,701
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
600–4,000 none
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
2 none
Grid
?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives.
uniform
Observations
?Exported spectral rows in this source, including repeated measurements when present.
1,976

Signal & quality

Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide

Value range
?Minimum and maximum recorded spectral values, in the original signal representation.
-0.82 – 2.62
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.251 – 1.68
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.8918
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
3032
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
1.434
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
0.00026628
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
3.4e+03
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
7e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
1.43
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.003328
Saturated
?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits.
0.0%
X-outliers
?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically.
999

Integrity & artefacts

Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide

NaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage.
0.00%
Inf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export.
0
Zero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing.
0.00%
Spike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged.
193,228
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
5.76%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
94,392
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
2.81%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide

Baseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit.
-0.94462
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0031816
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.007565
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.13632
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.31427
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.10167
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.21955
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.30729
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.14355
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.17353
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
0

Outliers & repeatability

Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide

PCA Q p95/median
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median.
3.8
Hotelling T2 p95/median
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median.
3.8
Mahalanobis H p95/median
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra.
1.9
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
0

Dimensionality (PCA)

Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide

Effective rank
?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions.
3
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
6
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
11
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
98.8%
Computed metric scores 30highest alert 1.00

Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/masked cell count (owner report)
?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.zero_ratio		2.98e-05%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectance
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.mean_reflectance		0.891761.00strongAtypical value: Too light / background visible or Too darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curve
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.area_under_curve		30321.00strongAtypical value: Illumination difference or NormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.peak_to_peak		1.43360.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.variance		0.191630.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxy
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_sigma_proxy		0.000266280.00weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_signal_to_noise_proxy		3355.20.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxy
?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.bandwise_roughness_signal_to_noise_proxy_min		78.9060.00weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_count		193,2281.00strongArtifactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_rate		5.76%1.00strongSuspicious spectrumInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_count		94,3921.00strongDetector connectionSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_rate		2.81%1.00strongSpectral problemCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.clip_fraction		5.95e-05%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.baseline_slope		-0.944621.00strongDriftIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.curvature_rms		0.00318160.22weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.d1_rms		0.0075650.11weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.pca_q_ratio		3.82240.48mediumAtypical spectrumArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.hotelling_t2_ratio		3.78270.47mediumExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis H
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.mahalanobis_h_ratio		1.94490.49mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrum
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.rms_to_mean_spectrum_p95		0.314270.88strongDifferent spectrumDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.sam_to_mean_spectrum_p95		0.219550.63mediumDifferent shapeBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.rms_intra_id		——not calculableNot enough information to score this metric on this dataset.Positioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.sam_intra_id		——not calculableNot enough information to score this metric on this dataset.Acquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.cv_intra_id		——not calculableNot enough information to score this metric on this dataset.Operatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score density
?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.pca_score_density		0.541770.70mediumSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)
?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.knn_distance_ratio_p95		2.09340.55mediumIsolated spectrumRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest score
?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.isolation_forest_score_p95		0.524670.70mediumAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source recovered_spectra: 800 of 1,976 source observations are plotted. PC1 captures 72.7% and PC2 12.3% of the reported spectral variance: 85.0% together; 15.0% remains outside this view.

Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.

recovered_spectra PCA score plot-40-200204060-10-5051015PC1 -7.956 · PC2 -0.2725PC1 4.062 · PC2 -0.2061PC1 3.392 · PC2 3.294PC1 1.316 · PC2 2.715PC1 3.19 · PC2 -0.02447PC1 5.213 · PC2 -0.09712PC1 2.98 · PC2 0.6055PC1 -4.834 · PC2 1.808PC1 -0.8391 · PC2 1.317PC1 0.0896 · PC2 -0.8086PC1 1.345 · PC2 -0.4397PC1 3.15 · PC2 -1.213PC1 1.597 · PC2 0.4529PC1 9.797 · PC2 -2.696PC1 -5.42 · PC2 2.044PC1 6.45 · PC2 -1.332PC1 5.566 · PC2 -0.4911PC1 1.674 · PC2 -2.281PC1 -0.9036 · PC2 -0.04668PC1 -0.7418 · PC2 0.1741PC1 -10.01 · PC2 1.516PC1 -5.463 · PC2 -3.339PC1 -14.26 · PC2 0.6846PC1 11.57 · PC2 -5.858PC1 2.628 · PC2 -4.645PC1 9.618 · PC2 -4.885PC1 5.767 · PC2 -4.679PC1 9.126 · PC2 -4.858PC1 1.092 · PC2 -8.195PC1 -4.716 · PC2 -6.762PC1 -2.545 · PC2 -7.472PC1 -7.304 · PC2 -6.266PC1 -5.163 · PC2 -8.272PC1 -6.299 · PC2 0.4353PC1 -6.33 · PC2 0.8117PC1 4.836 · PC2 0.8939PC1 1.58 · PC2 1.938PC1 5.005 · PC2 2.269PC1 7.763 · PC2 0.655PC1 1.211 · PC2 1.334PC1 0.9945 · PC2 1.468PC1 1.789 · PC2 0.9461PC1 3.398 · PC2 4.93PC1 18.05 · PC2 5.746PC1 4.109 · PC2 2.705PC1 -1.171 · PC2 1.618PC1 -7.745 · PC2 -2.549PC1 3.293 · PC2 0.9084PC1 1.215 · PC2 -0.9395PC1 5.116 · PC2 -2.455PC1 2.28 · PC2 3.288PC1 5.46 · PC2 -2.468PC1 -4.935 · PC2 -1.964PC1 0.3531 · PC2 -2.251PC1 6.145 · PC2 -3.667PC1 2.364 · PC2 2.361PC1 -1.034 · PC2 2.042PC1 0.6391 · PC2 -2.533PC1 0.4137 · PC2 -3.634PC1 -7.395 · PC2 0.02521PC1 -4.543 · PC2 1.096PC1 -2.509 · PC2 1.679PC1 2.266 · PC2 4.251PC1 -4.173 · PC2 3.932PC1 -1.577 · PC2 1.55PC1 -4.645 · PC2 2.227PC1 -8.322 · PC2 1.508PC1 1.336 · PC2 1.833PC1 -1.795 · PC2 1.712PC1 -5.431 · PC2 2.001PC1 -0.2597 · PC2 1.265PC1 6.015 · PC2 0.01751PC1 7.868 · PC2 -4.055PC1 12.31 · PC2 -6.164PC1 12.51 · PC2 -5.552PC1 10.85 · PC2 -6.272PC1 6.049 · PC2 -4.422PC1 5.43 · PC2 -5.185PC1 6.793 · PC2 -6.247PC1 9.415 · PC2 -3.287PC1 4.181 · PC2 -4.116PC1 6.183 · PC2 -3.337PC1 3.82 · PC2 -5.837PC1 -1.916 · PC2 2.255PC1 -0.8366 · PC2 6.182PC1 2.893 · PC2 -2.162PC1 3.122 · PC2 -0.807PC1 4.62 · PC2 -0.3197PC1 7.382 · PC2 -1.867PC1 7.575 · PC2 -0.8917PC1 0.339 · PC2 -3.558PC1 -5.31 · PC2 1.457PC1 -1.15 · PC2 -1.232PC1 -1.468 · PC2 -0.8513PC1 -0.7336 · PC2 -0.3656PC1 -4.521 · PC2 2.32PC1 2.119 · PC2 -5.067PC1 3.975 · PC2 -5.101PC1 2.411 · PC2 -5.077PC1 2.017 · PC2 -5.346PC1 0.2427 · PC2 -4.928PC1 2.736 · PC2 -5.022PC1 4.571 · PC2 -3.72PC1 3.243 · PC2 -4.215PC1 4.807 · PC2 -3.354PC1 1.145 · PC2 -4.616PC1 2.476 · PC2 -4.682PC1 0.04415 · PC2 -4.421PC1 0.6998 · PC2 -4.559PC1 1.34 · PC2 -4.005PC1 3.038 · PC2 -3.537PC1 3.389 · PC2 -2.765PC1 4.681 · PC2 -4.768PC1 2.6 · PC2 -4.286PC1 4.452 · PC2 -3.813PC1 -0.1762 · PC2 -4.346PC1 1.835 · PC2 -2.659PC1 -0.5963 · PC2 -4.928PC1 3.519 · PC2 -3.797PC1 1.273 · PC2 0.7116PC1 2.806 · PC2 1.479PC1 9.675 · PC2 4.546PC1 -6.089 · PC2 0.3519PC1 0.08515 · PC2 -0.3056PC1 0.9634 · PC2 -3.163PC1 3.98 · PC2 -4.254PC1 -8.48 · PC2 1.446PC1 0.7054 · PC2 -1.725PC1 -1.147 · PC2 -1.783PC1 -3.607 · PC2 2.606PC1 -12.36 · PC2 -1.051PC1 3.91 · PC2 0.979PC1 -10.41 · PC2 -0.7944PC1 -0.1809 · PC2 0.4863PC1 1.625 · PC2 1.634PC1 -2.163 · PC2 -0.1708PC1 5.227 · PC2 0.4484PC1 -1.021 · PC2 0.2985PC1 -13.36 · PC2 -3.533PC1 2.155 · PC2 -0.4828PC1 -6.495 · PC2 -0.5629PC1 -7.286 · PC2 -1.505PC1 -0.1723 · PC2 -1.821PC1 -11.74 · PC2 -0.5014PC1 -6.778 · PC2 1.379PC1 -6.376 · PC2 0.5937PC1 -7.181 · PC2 3.967PC1 2.667 · PC2 -6.337PC1 -3.519 · PC2 -0.3177PC1 1.041 · PC2 0.02149PC1 -8.667 · PC2 2.597PC1 -3.727 · PC2 -2.169PC1 -3.351 · PC2 -1.847PC1 -3.81 · PC2 -0.5482PC1 -1.305 · PC2 -1.052PC1 -5.448 · PC2 -0.6228PC1 2.275 · PC2 -1.137PC1 -0.9534 · PC2 1.768PC1 -1.789 · PC2 2.05PC1 -6.093 · PC2 -0.1581PC1 -4.696 · PC2 -0.1115PC1 1.921 · PC2 0.6249PC1 2.866 · PC2 0.08574PC1 -1.081 · PC2 -1.634PC1 -1.839 · PC2 0.6347PC1 -13.27 · PC2 -4.021PC1 -15.66 · PC2 -3.924PC1 -14.76 · PC2 1.533PC1 -10.19 · PC2 0.9176PC1 -11.36 · PC2 0.31PC1 -1.215 · PC2 -1.191PC1 -6.115 · PC2 -2.65PC1 -2.193 · PC2 -2.53PC1 -1.404 · PC2 -3.074PC1 -5.362 · PC2 -0.7139PC1 -2.034 · PC2 -1.132PC1 -4.865 · PC2 -0.7471PC1 -28.64 · PC2 -1.169PC1 3.176 · PC2 1.675PC1 11.51 · PC2 3.351PC1 -8.113 · PC2 -0.0118PC1 -6.133 · PC2 -3.182PC1 -2.818 · PC2 -1.641PC1 -9.298 · PC2 -0.5137PC1 -12.11 · PC2 0.2785PC1 -1.735 · PC2 1.174PC1 0.3649 · PC2 1.228PC1 -15.32 · PC2 4.181PC1 -9.993 · PC2 -1.233PC1 -8.462 · PC2 2.972PC1 -7.186 · PC2 -2.989PC1 -2.451 · PC2 -2.161PC1 -1.099 · PC2 -0.2383PC1 -7.813 · PC2 -1.725PC1 2.546 · PC2 1.24PC1 -4.233 · PC2 -1.752PC1 -11.55 · PC2 0.2671PC1 -10.49 · PC2 1.446PC1 -4.791 · PC2 -1.058PC1 -8.48 · PC2 0.4211PC1 -2.95 · PC2 0.3097PC1 -2.355 · PC2 0.5941PC1 -6.906 · PC2 1.148PC1 -7.576 · PC2 -1.762PC1 -4.17 · PC2 -2.168PC1 -3.527 · PC2 0.2671PC1 -4.127 · PC2 1.264PC1 -5.277 · PC2 3.018PC1 -4.921 · PC2 1.638PC1 -0.06832 · PC2 3.118PC1 -3.079 · PC2 -2.019PC1 5.077 · PC2 -2.35PC1 4.462 · PC2 4.256PC1 12.91 · PC2 6.722PC1 5.312 · PC2 5.19PC1 10.35 · PC2 6.256PC1 17.79 · PC2 7.411PC1 -1.3 · PC2 1.766PC1 17.42 · PC2 5.463PC1 14.68 · PC2 4.86PC1 2.524 · PC2 0.1368PC1 -2.127 · PC2 2.681PC1 34.51 · PC2 10.08PC1 46.18 · PC2 11.39PC1 39.83 · PC2 9.842PC1 41.35 · PC2 11.37PC1 -2.434 · PC2 1.979PC1 -3.492 · PC2 2.343PC1 -1.293 · PC2 1.006PC1 0.4475 · PC2 1.197PC1 0.5609 · PC2 1.381PC1 3.05 · PC2 3.18PC1 -5.915 · PC2 2.404PC1 1.342 · PC2 1.368PC1 2.971 · PC2 1.418PC1 5.525 · PC2 0.9872PC1 -2.345 · PC2 2.505PC1 -2.372 · PC2 2.471PC1 -0.8094 · PC2 3.757PC1 -4.356 · PC2 3.005PC1 -7.741 · PC2 2.821PC1 -6.789 · PC2 3.203PC1 -9.457 · PC2 -1.44PC1 -5.669 · PC2 1.095PC1 -4.286 · PC2 -0.8069PC1 -5.848 · PC2 -0.2119PC1 -7.234 · PC2 1.127PC1 -9.334 · PC2 0.2472PC1 -5.649 · PC2 1.336PC1 -5.625 · PC2 1.569PC1 -6.284 · PC2 0.3893PC1 -1.062 · PC2 -1.129PC1 -6.167 · PC2 2.05PC1 -4.393 · PC2 -1.308PC1 -6.98 · PC2 -1.176PC1 -7.635 · PC2 -1.079PC1 -7.887 · PC2 -0.3039PC1 -9.454 · PC2 0.18PC1 -3.299 · PC2 -1.391PC1 -6.45 · PC2 -1.09PC1 -7.787 · PC2 1.979PC1 -8.536 · PC2 1.938PC1 -8.955 · PC2 1.025PC1 -8.379 · PC2 1.473PC1 -6.484 · PC2 1.992PC1 -4.245 · PC2 2.796PC1 4.586 · PC2 2.238PC1 -1.831 · PC2 -1.724PC1 -9.123 · PC2 2.335PC1 3.766 · PC2 3.119PC1 -0.9565 · PC2 1.726PC1 -5.554 · PC2 2.795PC1 -15.87 · PC2 1.633PC1 -13.71 · PC2 2.088PC1 -17.91 · PC2 4.441PC1 -10.44 · PC2 3.487PC1 4.531 · PC2 -4.1PC1 -7.588 · PC2 0.1988PC1 -0.7417 · PC2 -1.43PC1 -0.7042 · PC2 -1.208PC1 -2.752 · PC2 0.6972PC1 3.466 · PC2 -3.75PC1 6.534 · PC2 -4.538PC1 8.309 · PC2 -5.37PC1 1.483 · PC2 -3.447PC1 9.11 · PC2 -4.317PC1 -1.083 · PC2 -5.068PC1 9.52 · PC2 -6.89PC1 7.209 · PC2 -5.845PC1 0.196 · PC2 -3.6PC1 3.552 · PC2 -3.503PC1 3.672 · PC2 -3.838PC1 -3.122 · PC2 -5.107PC1 -7.803 · PC2 -4.13PC1 -12.18 · PC2 -3.914PC1 -7.535 · PC2 -2.55PC1 -6.571 · PC2 -2.279PC1 3.4 · PC2 -0.8313PC1 -7.66 · PC2 -2.39PC1 0.9514 · PC2 -1.537PC1 -3.757 · PC2 -0.5034PC1 3.135 · PC2 -4.203PC1 -8.256 · PC2 -3.218PC1 2.815 · PC2 -4.38PC1 1.648 · PC2 -3.492PC1 0.117 · PC2 -1.52PC1 -8.941 · PC2 2.348PC1 -3.061 · PC2 -0.8746PC1 -8 · PC2 5.966PC1 -4.71 · PC2 -0.9941PC1 -6.027 · PC2 2.859PC1 -7.713 · PC2 -0.3304PC1 1.141 · PC2 2.057PC1 -17.87 · PC2 -3.02PC1 -29.66 · PC2 -7.619PC1 -4.389 · PC2 -1.395PC1 -3.2 · PC2 -1.241PC1 -0.3769 · PC2 1.343PC1 0.7133 · PC2 -2.341PC1 -0.005961 · PC2 2.72PC1 -1.906 · PC2 -1.278PC1 -3.7 · PC2 2.099PC1 5.861 · PC2 -6.505PC1 -17.1 · PC2 -1.457PC1 -8.375 · PC2 0.2634PC1 -3.168 · PC2 0.5137PC1 -7.384 · PC2 4.894PC1 1.445 · PC2 1.871PC1 1.293 · PC2 2.914PC1 -0.4085 · PC2 3.525PC1 -11.53 · PC2 2.059PC1 0.5705 · PC2 2.311PC1 1.668 · PC2 0.5942PC1 -3.957 · PC2 2.981PC1 -4.296 · PC2 -2.657PC1 -1.591 · PC2 -1.622PC1 0.2514 · PC2 -2.032PC1 -0.6424 · PC2 -0.9473PC1 -2.852 · PC2 -2.322PC1 -1.622 · PC2 -0.06181PC1 0.9163 · PC2 -1.751PC1 -5.48 · PC2 -2.342PC1 -11.36 · PC2 0.002311PC1 -9.915 · PC2 0.6542PC1 -9.434 · PC2 0.8756PC1 -0.4378 · PC2 1.745PC1 3.32 · PC2 -0.6691PC1 0.2609 · PC2 1.654PC1 -0.9596 · PC2 -0.1604PC1 6.028 · PC2 -0.9939PC1 1.05 · PC2 -2.962PC1 -3.523 · PC2 -3.352PC1 2.299 · PC2 2.177PC1 -4.905 · PC2 4.651PC1 6.881 · PC2 3.689PC1 3.714 · PC2 -2.301PC1 5.259 · PC2 -2.582PC1 5.789 · PC2 -2.149PC1 0.4101 · PC2 1.554PC1 3.062 · PC2 0.6686PC1 5.231 · PC2 0.4354PC1 -0.02527 · PC2 2.418PC1 12.93 · PC2 5.788PC1 -7.401 · PC2 -0.1247PC1 -1.483 · PC2 1.731PC1 -2.294 · PC2 0.7599PC1 -6.361 · PC2 1.124PC1 -3.612 · PC2 1.688PC1 -4.366 · PC2 1.614PC1 -3.942 · PC2 1.495PC1 -8.255 · PC2 -0.5817PC1 -2.07 · PC2 -0.5496PC1 2.742 · PC2 -1.915PC1 -2.018 · PC2 -0.7716PC1 -7.664 · PC2 1.298PC1 -25.72 · PC2 -2.55PC1 -15.45 · PC2 -0.1239PC1 -8.106 · PC2 -2.227PC1 -9.424 · PC2 -1.797PC1 -4.712 · PC2 -0.3054PC1 0.8172 · PC2 -0.8406PC1 1.302 · PC2 1.012PC1 2.809 · PC2 0.07493PC1 3.058 · PC2 -0.6345PC1 1.731 · PC2 -1.369PC1 0.09843 · PC2 -6.995PC1 -8.296 · PC2 -5.724PC1 0.9842 · PC2 2.408PC1 -4.319 · PC2 2.581PC1 -3.263 · PC2 0.6843PC1 2.523 · PC2 -3.916PC1 -9.082 · PC2 0.8367PC1 -3.147 · PC2 -1.686PC1 1.854 · PC2 0.5074PC1 0.8878 · PC2 0.262PC1 2.337 · PC2 0.7802PC1 1.227 · PC2 0.1691PC1 0.8404 · PC2 0.3573PC1 0.03255 · PC2 0.003377PC1 0.9539 · PC2 0.27PC1 1.888 · PC2 -0.1473PC1 2.693 · PC2 0.262PC1 1.953 · PC2 0.5375PC1 -0.01403 · PC2 0.1177PC1 2.196 · PC2 1.386PC1 9.044 · PC2 3.76PC1 4.992 · PC2 -0.3926PC1 5.105 · PC2 3.163PC1 0.849 · PC2 0.4981PC1 0.4428 · PC2 0.8421PC1 4.818 · PC2 -0.8952PC1 9.565 · PC2 -2.618PC1 7.336 · PC2 -3.65PC1 6.237 · PC2 -4.569PC1 2.303 · PC2 2.343PC1 -3.361 · PC2 0.74PC1 -0.06217 · PC2 2.796PC1 10.31 · PC2 -4.558PC1 -4.236 · PC2 3.247PC1 11.9 · PC2 -8.21PC1 -1.273 · PC2 2.63PC1 -6.523 · PC2 2.919PC1 -2.615 · PC2 2.945PC1 -3.547 · PC2 3.729PC1 6.054 · PC2 -2.024PC1 -3.332 · PC2 -1.453PC1 1.363 · PC2 1.593PC1 8.608 · PC2 -0.6516PC1 -6.264 · PC2 1.322PC1 6.576 · PC2 -2.253PC1 9.751 · PC2 -4.501PC1 8.059 · PC2 -3.864PC1 3.16 · PC2 0.4516PC1 1.71 · PC2 -0.002068PC1 4.785 · PC2 0.7128PC1 -2.084 · PC2 -0.9571PC1 -10.86 · PC2 0.5999PC1 -6.147 · PC2 4.192PC1 0.2463 · PC2 0.3657PC1 -4.199 · PC2 1.266PC1 8.098 · PC2 -3.574PC1 5.989 · PC2 -1.245PC1 2.17 · PC2 -1.458PC1 4.198 · PC2 -0.8359PC1 -8.068 · PC2 1.106PC1 -1.497 · PC2 0.06763PC1 -8.884 · PC2 0.1575PC1 5.041 · PC2 -2.335PC1 4.467 · PC2 0.5319PC1 2.812 · PC2 0.7319PC1 -0.143 · PC2 -0.1679PC1 -1.416 · PC2 0.1895PC1 0.2963 · PC2 0.5951PC1 9.86 · PC2 -6.516PC1 -4.567 · PC2 2.457PC1 -1.486 · PC2 3.099PC1 1.908 · PC2 5.504PC1 4.736 · PC2 2.821PC1 3.013 · PC2 -1.806PC1 2.52 · PC2 -1.548PC1 3.379 · PC2 -2.126PC1 -6.488 · PC2 -0.2681PC1 1.869 · PC2 -1.043PC1 -8.175 · PC2 -0.9642PC1 -1.976 · PC2 4.156PC1 2.513 · PC2 0.8629PC1 -0.4161 · PC2 0.1545PC1 0.0858 · PC2 -0.004628PC1 5.255 · PC2 0.8394PC1 0.05051 · PC2 3.308PC1 6.857 · PC2 -6.688PC1 10.02 · PC2 -6.086PC1 10.61 · PC2 -1.887PC1 5.503 · PC2 -2.99PC1 0.5261 · PC2 0.6266PC1 2.701 · PC2 1.315PC1 1.631 · PC2 2.106PC1 5.693 · PC2 -0.172PC1 -3.391 · PC2 -2.05PC1 -0.8903 · PC2 -1.632PC1 3.969 · PC2 -3.65PC1 1.523 · PC2 0.3266PC1 -2.289 · PC2 0.4721PC1 -0.7782 · PC2 0.6859PC1 -3.94 · PC2 0.9821PC1 -9.655 · PC2 -0.1616PC1 -5.02 · PC2 0.9969PC1 0.4241 · PC2 0.3072PC1 9.962 · PC2 -4.156PC1 1.068 · PC2 2.52PC1 -2.144 · PC2 2.321PC1 2.983 · PC2 5.298PC1 2.18 · PC2 4.103PC1 -5.027 · PC2 0.8876PC1 1.863 · PC2 3.585PC1 1.871 · PC2 0.3787PC1 4.197 · PC2 0.8108PC1 -1.476 · PC2 1.515PC1 -20.56 · PC2 -3.551PC1 -13.06 · PC2 -1.118PC1 0.4312 · PC2 1.716PC1 -10.25 · PC2 2.017PC1 0.8154 · PC2 0.9133PC1 5.461 · PC2 -0.1368PC1 5.789 · PC2 0.2462PC1 -2.92 · PC2 0.923PC1 -2.588 · PC2 1.315PC1 0.4981 · PC2 1.325PC1 -2.487 · PC2 1.181PC1 -2.51 · PC2 0.9567PC1 -2.716 · PC2 1.239PC1 -0.8864 · PC2 0.9931PC1 -1.699 · PC2 1.026PC1 1.308 · PC2 -0.0677PC1 1.722 · PC2 0.1946PC1 0.407 · PC2 0.7848PC1 -0.2786 · PC2 1.297PC1 0.5245 · PC2 0.3333PC1 0.4979 · PC2 0.1293PC1 0.272 · PC2 0.1572PC1 -8.273 · PC2 3.77PC1 3.753 · PC2 3.952PC1 -0.8636 · PC2 1.753PC1 -2.599 · PC2 2.545PC1 -2.794 · PC2 0.8128PC1 -2.282 · PC2 3.395PC1 0.1966 · PC2 3.564PC1 2.067 · PC2 1.723PC1 2.763 · PC2 1.275PC1 -0.4316 · PC2 3.565PC1 10.56 · PC2 -5.539PC1 2.676 · PC2 0.8642PC1 1.751 · PC2 1.759PC1 -0.3518 · PC2 1.807PC1 -0.1475 · PC2 0.03215PC1 1.809 · PC2 -0.5782PC1 0.5038 · PC2 0.269PC1 5.052 · PC2 0.928PC1 -1.958 · PC2 0.7692PC1 -3.725 · PC2 0.7896PC1 -1.374 · PC2 0.3923PC1 -3.286 · PC2 1.283PC1 7.226 · PC2 2.732PC1 3.194 · PC2 1.367PC1 1.128 · PC2 -0.09959PC1 3.998 · PC2 0.468PC1 4.354 · PC2 0.05465PC1 7.538 · PC2 0.3359PC1 -4.466 · PC2 1.717PC1 -6.946 · PC2 2.081PC1 -5.377 · PC2 2.274PC1 -2.501 · PC2 3.983PC1 5.657 · PC2 -0.5119PC1 4.326 · PC2 2.083PC1 7.034 · PC2 1.088PC1 10.12 · PC2 -1.001PC1 8.252 · PC2 -0.3075PC1 4.165 · PC2 -1.158PC1 -0.4528 · PC2 0.7265PC1 5.341 · PC2 -0.5037PC1 2.817 · PC2 0.4901PC1 0.41 · PC2 0.1819PC1 -0.4482 · PC2 3.571PC1 3.599 · PC2 0.5833PC1 5.25 · PC2 -0.9789PC1 -1.777 · PC2 0.9623PC1 3.054 · PC2 0.4571PC1 -2.586 · PC2 2.977PC1 -4.334 · PC2 1.55PC1 -0.7477 · PC2 3.107PC1 -0.9567 · PC2 0.9955PC1 0.325 · PC2 1.41PC1 -2.242 · PC2 0.7515PC1 -4.602 · PC2 -2.644PC1 -9.667 · PC2 -3.682PC1 2.915 · PC2 -2.517PC1 12.71 · PC2 -1.004PC1 12.43 · PC2 -0.6806PC1 4.038 · PC2 -1.92PC1 3.144 · PC2 -1.497PC1 8.289 · PC2 -0.6101PC1 3.422 · PC2 -1.245PC1 0.2148 · PC2 1.898PC1 0.3339 · PC2 0.3244PC1 -1.937 · PC2 1.407PC1 0.4991 · PC2 0.2593PC1 2.302 · PC2 -0.02471PC1 3.695 · PC2 1.421PC1 0.6058 · PC2 -0.791PC1 -3.134 · PC2 2.254PC1 -0.779 · PC2 -2.937PC1 -3.416 · PC2 -1.005PC1 1.617 · PC2 1.995PC1 5.796 · PC2 0.003927PC1 7.202 · PC2 0.8854PC1 -0.8265 · PC2 2.391PC1 2.468 · PC2 -0.01034PC1 0.5204 · PC2 0.3284PC1 3.424 · PC2 0.1594PC1 2.557 · PC2 3.638PC1 -1.744 · PC2 2.845PC1 6.468 · PC2 3.613PC1 1.302 · PC2 0.3042PC1 5.411 · PC2 -0.3855PC1 3.275 · PC2 -0.07723PC1 -1.063 · PC2 1.272PC1 4.926 · PC2 1.523PC1 -3.827 · PC2 3.509PC1 -7.067 · PC2 1.254PC1 0.7027 · PC2 -0.1637PC1 -2.346 · PC2 0.06472PC1 5.411 · PC2 -6.519PC1 9.076 · PC2 -5.382PC1 5.938 · PC2 -2.626PC1 4.782 · PC2 -4.84PC1 -0.5335 · PC2 -0.721PC1 0.8902 · PC2 -0.4347PC1 0.7762 · PC2 -0.6647PC1 -2.266 · PC2 -0.01805PC1 -4.95 · PC2 0.3042PC1 0.8879 · PC2 0.2277PC1 -0.6261 · PC2 0.7635PC1 9.255 · PC2 -0.2374PC1 1.767 · PC2 1.28PC1 -13.09 · PC2 -2.918PC1 -8.127 · PC2 2.215PC1 -11.54 · PC2 3.234PC1 2.834 · PC2 0.3989PC1 7.503 · PC2 1.13PC1 -0.5947 · PC2 -0.8363PC1 -2.7 · PC2 0.6376PC1 11.88 · PC2 -5.165PC1 4.625 · PC2 -3.781PC1 9.678 · PC2 -3.841PC1 4.361 · PC2 0.2779PC1 3.078 · PC2 -1.671PC1 10.86 · PC2 -3.743PC1 8.66 · PC2 -1.993PC1 -1.384 · PC2 -0.7133PC1 -5.821 · PC2 -0.3049PC1 -0.7099 · PC2 -0.7806PC1 6.608 · PC2 2.549PC1 1.725 · PC2 -2.817PC1 6.808 · PC2 -2.966PC1 3.631 · PC2 -2.224PC1 9.978 · PC2 -3.102PC1 9.571 · PC2 -3.525PC1 11.09 · PC2 -3.783PC1 11.13 · PC2 -4.632PC1 7.575 · PC2 -3.013PC1 3.325 · PC2 -2.86PC1 6.69 · PC2 -2.623PC1 -2.94 · PC2 1.172PC1 -3.588 · PC2 1.949PC1 -5.905 · PC2 2.459PC1 -5.289 · PC2 0.2805PC1 0.5359 · PC2 -0.6608PC1 -6.526 · PC2 0.9484PC1 -2.527 · PC2 -0.2405PC1 -3.755 · PC2 -0.1522PC1 -1.028 · PC2 2.062PC1 -3.746 · PC2 3.563PC1 -1.007 · PC2 3.696PC1 -3.213 · PC2 1.669PC1 9.743 · PC2 -1.923PC1 -9.084 · PC2 1.521PC1 -4.539 · PC2 3.338PC1 4.499 · PC2 2.778PC1 1.98 · PC2 2.557PC1 3.636 · PC2 0.02739PC1 1.044 · PC2 -1.44PC1 -1.832 · PC2 2.797PC1 3.208 · PC2 1.212PC1 -1.515 · PC2 -0.1894PC1 -2.929 · PC2 -2.476PC1 5.329 · PC2 -0.7972PC1 7.745 · PC2 3.746PC1 -0.1805 · PC2 -0.1701PC1 8.065 · PC2 4.392PC1 -3.694 · PC2 -0.2012PC1 5.611 · PC2 3.093PC1 -5.247 · PC2 4.899PC1 0.8468 · PC2 1.02PC1 -1.077 · PC2 0.4207PC1 2.233 · PC2 -0.4974PC1 -3.815 · PC2 1.304PC1 -2.009 · PC2 3.564PC1 -3.538 · PC2 4.578PC1 10.38 · PC2 5.933PC1 9.534 · PC2 5.983PC1 -6.653 · PC2 1.67PC1 1.408 · PC2 -2.494PC1 5.75 · PC2 -1.918PC1 7.067 · PC2 -1.451PC1 -9.017 · PC2 1.215PC1 -6.704 · PC2 2.066PC1 4.082 · PC2 -0.8796PC1 -1.435 · PC2 -0.3935PC1 8.513 · PC2 2.58PC1 2.854 · PC2 1.067PC1 -10.97 · PC2 3.228PC1 -0.4341 · PC2 4.697PC1 -0.3294 · PC2 2.157PC1 -2.647 · PC2 1.021PC1 -2.307 · PC2 2.358PC1 -0.8966 · PC2 2.79PC1 -2.638 · PC2 3.495PC1 -6.696 · PC2 4.32PC1 -1.693 · PC2 2.333PC1 -1.735 · PC2 3.153PC1 0.3879 · PC2 2.873PC1 -3.61 · PC2 -0.4888PC1 3.947 · PC2 0.6879PC1 3.647 · PC2 -0.3573PC1 5.232 · PC2 -0.0009998PC1 -0.9092 · PC2 -0.1842PC1 0.5684 · PC2 0.1546PC1 4.597 · PC2 1.143PC1 0.06704 · PC2 0.2245PC1 -0.2052 · PC2 1.164PC1 1.203 · PC2 -0.02078PC1 2.533 · PC2 -2.623PC1 0.2977 · PC2 -2.19PC1 -1.839 · PC2 -1.968PC1 3.976 · PC2 -0.102PC1 9.518 · PC2 2.25PC1 -1.016 · PC2 1.302PC1 -0.3356 · PC2 4.464PC1 3.241 · PC2 -1.192PC1 3.675 · PC2 -3.209PC1 3.158 · PC2 -1.104PC1 2.578 · PC2 -3.527PC1 10.9 · PC2 -5.082PC1 -3.299 · PC2 -4.341PC1 0.349 · PC2 4.968PC1 5.127 · PC2 -2.555PC1 11.58 · PC2 -5.025PC1 5.323 · PC2 -4.914PC1 4.681 · PC2 -3.541PC1 3.373 · PC2 -3.011PC1 5.397 · PC2 0.1374PC1 -3.957 · PC2 1.194PC1 -2.574 · PC2 1.435PC1 -6.238 · PC2 -0.5507PC1 -5.564 · PC2 1.997PC1 1.743 · PC2 -0.3117PC1 -9.902 · PC2 0.9263PC1 -6.991 · PC2 0.3609PC1 3.844 · PC2 2.804PC1 8.543 · PC2 1.283PC1 3.202 · PC2 1.568PC1 1.897 · PC2 1.446PC1 5.115 · PC2 2.102PC1 7.013 · PC2 1.668PC1 0.7363 · PC2 0.8988PC1 4.12 · PC2 -0.04287PC1 4.378 · PC2 0.9521PC1 5.727 · PC2 0.9563PC1 2.133 · PC2 0.8538PC1 0.699 · PC2 1.134PC1 -6.966 · PC2 1.699PC1 10.36 · PC2 3.484PC1 -5.283 · PC2 2.472PC1 4.444 · PC2 -0.3456PC1 3.398 · PC2 0.5845PC1 6.15 · PC2 0.04738PC1 -1.79 · PC2 3.437PC1 3.244 · PC2 2.335PC1 -5.696 · PC2 1.179PC1 -4.606 · PC2 -0.09542PC1 -1.385 · PC2 -0.8207PC1 5.433 · PC2 0.7716PC1 -1.938 · PC2 1.695PC1 1.718 · PC2 -0.3349PC1 3.772 · PC2 1.55PC1 5.567 · PC2 -0.1793PC1 6.123 · PC2 -0.9073PC1 0.471 · PC2 -0.0445PC1 1.646 · PC2 1.394PC1 0.243 · PC2 0.7513PC1 -2.055 · PC2 1.453PC1 6.667 · PC2 -0.3969PC1 3.217 · PC2 0.5371PC1 -0.1405 · PC2 0.08237PC1 -1.039 · PC2 0.1052PC1 -0.5615 · PC2 -0.4543PC1 1.605 · PC2 3.008PC1 2.277 · PC2 0.9056PC1 -5.324 · PC2 2.101PC1 3.776 · PC2 -0.1362PC1 -6.632 · PC2 0.3384PC1 6.622 · PC2 -0.03639PC1 -5.988 · PC2 2.077PC1 8.222 · PC2 0.9806PC1 0.3976 · PC2 0.3036PC1 4.111 · PC2 -0.6103PC1 -2.325 · PC2 2.017PC1 0.8111 · PC2 1.566PC1 -0.5959 · PC2 1.304PC1 -0.4547 · PC2 1.754PC1 (72.7%)PC2 (12.3%)800 scores
View plotted PCA coordinates

Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
1-7.9561-0.27254
24.0622-0.20612
33.39253.2935
41.31622.7154
53.1901-0.02447
65.2134-0.097119
72.98010.60547
8-4.83381.8083
9-0.839151.3166
100.089601-0.8086
111.3448-0.43974
123.1505-1.2133
131.59730.45294
149.7974-2.6957
15-5.41992.0441
166.4497-1.3324
175.5664-0.49108
181.6737-2.2813
19-0.90364-0.046676
20-0.741780.17414
21-10.0061.5164
22-5.463-3.339
23-14.2640.68455
2411.566-5.8585
252.6281-4.6453
269.6184-4.8849
275.7668-4.6788
289.1261-4.8577
291.092-8.1953
30-4.7162-6.7617
31-2.5453-7.4716
32-7.3038-6.2661
33-5.1628-8.2723
34-6.29880.43532
35-6.32990.81167
364.83590.89393
371.57971.9383
385.00482.2693
397.76290.65496
401.21091.3341
410.994461.4681
421.78890.94611
433.39794.9301
4418.0535.7459
454.10932.7048
46-1.17081.6185
47-7.7453-2.549
483.29280.90839
491.215-0.93951
505.1165-2.455
512.28043.2877
525.4603-2.4678
53-4.9353-1.9641
540.35308-2.2512
556.1449-3.6675
562.36412.3613
57-1.03442.0425
580.63912-2.5332
590.41373-3.6336
60-7.39510.02521
61-4.54281.0956
62-2.50931.6786
632.26564.2511
64-4.17333.9316
65-1.57731.5498
66-4.64542.2271
67-8.32211.5079
681.33611.8333
69-1.79511.7116
70-5.43132.0011
71-0.259721.2651
726.01550.017508
737.8678-4.0548
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793-5.98782.0773
7948.22160.98057
7950.397610.30355
7964.1113-0.61028
797-2.32532.0166
7980.811151.566
799-0.595921.3041
800-0.45471.7538

PCA explained variance — how many dimensions?

Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.

PCA explained variance0%25%50%75%100%PC1: 73.0% (cumulative 73.0%)1PC2: 11.6% (cumulative 84.6%)2PC3: 4.1% (cumulative 88.7%)3PC4: 3.5% (cumulative 92.3%)4PC5: 2.5% (cumulative 94.7%)5PC6: 1.9% (cumulative 96.6%)6PC7: 0.9% (cumulative 97.6%)7PC8: 0.5% (cumulative 98.1%)8PC9: 0.4% (cumulative 98.5%)9PC10: 0.3% (cumulative 98.8%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC173.05%
PC211.58%
PC34.11%
PC43.53%
PC52.46%
PC61.89%
PC70.94%
PC80.52%
PC90.41%
PC100.31%
X-Y spectral correlation 20

Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.

Spectral association with oc_usda_c729_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_mir · oc_usda_c729_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,0004,000|r|signed raxis · Pearson correlation scale

Spectral association with c_tot_usda_a622_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_mir · c_tot_usda_a622_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,0004,000|r|signed raxis · Pearson correlation scale

Spectral association with n_tot_usda_a623_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_mir · n_tot_usda_a623_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,0004,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
oc_usda_c729_w_pct0.4382,9200.1940.0%
c_tot_usda_a622_w_pct0.4172,9200.1930.0%
n_tot_usda_a623_w_pct0.3982,9200.1940.0%
s_tot_usda_a624_w_pct0.2442,2320.0790.0%
ph_h2o_usda_a268_index0.541,3580.1761.9%
bd_usda_a4_g_cm30.4821,2260.2650.0%
clay_tot_usda_a334_w_pct0.6993,6500.29421.7%
silt_tot_usda_c62_w_pct0.281,9020.130.0%
sand_tot_usda_c60_w_pct0.5383,6300.231.5%
caco3_usda_a54_w_pct0.7121,3020.1474.9%
cec_usda_a723_cmolc_kg0.5543,5680.34720.1%
ca_ext_usda_a722_cmolc_kg0.5051,3540.1350.8%
mg_ext_usda_a724_cmolc_kg0.3161,3600.1410.0%
k_ext_usda_a725_cmolc_kg0.3121,2220.1320.0%
na_ext_usda_a726_cmolc_kg0.1271,3740.03790.0%
wr_33kPa_usda_a415_w_pct0.5182,9400.35820.3%
wr_1500kPa_usda_a417_w_pct0.5661,1720.36423.6%
al_dith_usda_a65_w_pct0.5142,7900.2814.6%
p_ext_usda_a652_mg_kg0.1272,3600.04760.0%
p_ext_usda_a1070_mg_kg0.1973,6800.06540.0%

recovered_spectra

X_nir · NIR · unknown

Read the signal and variation summaries for this source. Axis unit: none; signal representation: absorbance.

Spectral signal summary

The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.

recovered_spectra spectra0.20.40.60.81.01,0001,5002,0002,5003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / none1,350none — median 0.4508 (q25–q75 0.3694–0.5356)1,358none — median 0.4527 (q25–q75 0.3712–0.5375)1,368none — median 0.4545 (q25–q75 0.3729–0.5389)1,376none — median 0.4549 (q25–q75 0.3738–0.5391)1,384none — median 0.4526 (q25–q75 0.3714–0.5346)1,394none — median 0.4469 (q25–q75 0.3678–0.5277)1,402none — median 0.4405 (q25–q75 0.3629–0.5208)1,410none — median 0.4327 (q25–q75 0.3571–0.5128)1,420none — median 0.4373 (q25–q75 0.36–0.5172)1,428none — median 0.4471 (q25–q75 0.3678–0.529)1,436none — median 0.4531 (q25–q75 0.3723–0.5351)1,444none — median 0.4569 (q25–q75 0.376–0.5391)1,454none — median 0.4618 (q25–q75 0.3802–0.5449)1,462none — median 0.4645 (q25–q75 0.3824–0.5475)1,470none — median 0.468 (q25–q75 0.3855–0.5514)1,480none — median 0.4732 (q25–q75 0.3897–0.5565)1,488none — median 0.4762 (q25–q75 0.392–0.5602)1,496none — median 0.48 (q25–q75 0.3952–0.5642)1,506none — median 0.4844 (q25–q75 0.3996–0.5696)1,514none — median 0.4878 (q25–q75 0.4023–0.5732)1,522none — median 0.4914 (q25–q75 0.4055–0.5772)1,532none — median 0.4955 (q25–q75 0.4089–0.5822)1,540none — median 0.4983 (q25–q75 0.411–0.5848)1,548none — median 0.501 (q25–q75 0.4134–0.5883)1,558none — median 0.5051 (q25–q75 0.4166–0.5929)1,566none — median 0.508 (q25–q75 0.4189–0.5961)1,574none — median 0.5109 (q25–q75 0.4216–0.599)1,584none — median 0.5149 (q25–q75 0.4247–0.6033)1,592none — median 0.5178 (q25–q75 0.427–0.6069)1,600none — median 0.5206 (q25–q75 0.4295–0.6101)1,608none — median 0.5237 (q25–q75 0.4318–0.6138)1,618none — median 0.5276 (q25–q75 0.4351–0.6177)1,626none — median 0.5305 (q25–q75 0.4373–0.621)1,634none — median 0.5333 (q25–q75 0.4393–0.624)1,644none — median 0.5367 (q25–q75 0.4425–0.6278)1,652none — median 0.5396 (q25–q75 0.4452–0.6312)1,660none — median 0.5427 (q25–q75 0.4481–0.6345)1,670none — median 0.5465 (q25–q75 0.4515–0.6385)1,678none — median 0.5493 (q25–q75 0.4542–0.6418)1,686none — median 0.552 (q25–q75 0.4566–0.6445)1,696none — median 0.5559 (q25–q75 0.4597–0.6486)1,704none — median 0.5586 (q25–q75 0.4617–0.6515)1,712none — median 0.5607 (q25–q75 0.4635–0.6538)1,722none — median 0.5628 (q25–q75 0.4657–0.6558)1,730none — median 0.5653 (q25–q75 0.4673–0.658)1,738none — median 0.5678 (q25–q75 0.4693–0.6607)1,748none — median 0.57 (q25–q75 0.4709–0.6632)1,756none — median 0.5714 (q25–q75 0.4722–0.665)1,764none — median 0.5735 (q25–q75 0.4739–0.6672)1,774none — median 0.5756 (q25–q75 0.4756–0.6701)1,782none — median 0.5773 (q25–q75 0.4768–0.6715)1,790none — median 0.5786 (q25–q75 0.4777–0.673)1,798none — median 0.5808 (q25–q75 0.4793–0.6753)1,808none — median 0.5827 (q25–q75 0.4813–0.6778)1,816none — median 0.584 (q25–q75 0.4822–0.6791)1,824none — median 0.5854 (q25–q75 0.483–0.6804)1,834none — median 0.5874 (q25–q75 0.4845–0.6832)1,842none — median 0.5883 (q25–q75 0.4856–0.6847)1,850none — median 0.5882 (q25–q75 0.4854–0.684)1,860none — median 0.5866 (q25–q75 0.4842–0.6816)1,868none — median 0.5839 (q25–q75 0.4821–0.678)1,876none — median 0.5761 (q25–q75 0.4739–0.6688)1,886none — median 0.5508 (q25–q75 0.4519–0.6453)1,894none — median 0.5214 (q25–q75 0.426–0.6183)1,902none — median 0.4946 (q25–q75 0.3995–0.5924)1,912none — median 0.4769 (q25–q75 0.3821–0.5736)1,920none — median 0.4774 (q25–q75 0.3848–0.5735)1,928none — median 0.4857 (q25–q75 0.3949–0.5813)1,938none — median 0.4965 (q25–q75 0.4042–0.5916)1,946none — median 0.5028 (q25–q75 0.4095–0.5975)1,954none — median 0.5076 (q25–q75 0.4144–0.6023)1,962none — median 0.5132 (q25–q75 0.4203–0.6078)1,972none — median 0.5221 (q25–q75 0.4278–0.6163)1,980none — median 0.5286 (q25–q75 0.4333–0.6228)1,988none — median 0.5341 (q25–q75 0.4383–0.6282)1,998none — median 0.5408 (q25–q75 0.4442–0.635)2,006none — median 0.5463 (q25–q75 0.4498–0.6407)2,014none — median 0.5521 (q25–q75 0.4548–0.6461)2,024none — median 0.5571 (q25–q75 0.4598–0.6518)2,032none — median 0.5595 (q25–q75 0.4619–0.6547)2,040none — median 0.5618 (q25–q75 0.4632–0.6569)2,050none — median 0.5638 (q25–q75 0.4641–0.6593)2,058none — median 0.5656 (q25–q75 0.4655–0.6614)2,066none — median 0.5674 (q25–q75 0.4673–0.6634)2,076none — median 0.5688 (q25–q75 0.4678–0.6644)2,084none — median 0.5686 (q25–q75 0.4673–0.6639)2,092none — median 0.5682 (q25–q75 0.4668–0.6635)2,102none — median 0.5678 (q25–q75 0.467–0.6635)2,110none — median 0.5684 (q25–q75 0.4676–0.6637)2,118none — median 0.5685 (q25–q75 0.4678–0.6642)2,126none — median 0.5673 (q25–q75 0.467–0.6628)2,136none — median 0.5645 (q25–q75 0.464–0.6596)2,144none — median 0.5614 (q25–q75 0.4608–0.656)2,152none — median 0.5577 (q25–q75 0.4574–0.6523)2,162none — median 0.5521 (q25–q75 0.4527–0.6459)2,170none — median 0.5464 (q25–q75 0.448–0.6393)2,178none — median 0.5393 (q25–q75 0.4424–0.6305)2,188none — median 0.5288 (q25–q75 0.4338–0.6191)2,196none — median 0.5217 (q25–q75 0.4282–0.6109)2,204none — median 0.5183 (q25–q75 0.425–0.6067)2,214none — median 0.5196 (q25–q75 0.4265–0.6078)2,222none — median 0.5232 (q25–q75 0.4303–0.6126)2,230none — median 0.5279 (q25–q75 0.4351–0.6184)2,240none — median 0.5312 (q25–q75 0.4371–0.6232)2,248none — median 0.5318 (q25–q75 0.4373–0.6237)2,256none — median 0.5305 (q25–q75 0.4354–0.6223)2,266none — median 0.5277 (q25–q75 0.4325–0.62)2,274none — median 0.5252 (q25–q75 0.43–0.617)2,282none — median 0.5227 (q25–q75 0.4277–0.6145)2,292none — median 0.5191 (q25–q75 0.4237–0.6105)2,300none — median 0.5151 (q25–q75 0.4201–0.606)2,308none — median 0.5099 (q25–q75 0.4158–0.6004)2,316none — median 0.505 (q25–q75 0.4114–0.5945)2,326none — median 0.4984 (q25–q75 0.4069–0.5874)2,334none — median 0.4944 (q25–q75 0.4034–0.5822)2,342none — median 0.4905 (q25–q75 0.4001–0.5777)2,352none — median 0.4862 (q25–q75 0.396–0.5731)2,360none — median 0.4827 (q25–q75 0.3932–0.57)2,368none — median 0.48 (q25–q75 0.3903–0.5674)2,378none — median 0.4761 (q25–q75 0.3861–0.5638)2,386none — median 0.4735 (q25–q75 0.3835–0.5613)2,394none — median 0.4705 (q25–q75 0.3812–0.5589)2,404none — median 0.4665 (q25–q75 0.3769–0.5547)2,412none — median 0.4618 (q25–q75 0.3727–0.5503)2,420none — median 0.4566 (q25–q75 0.368–0.5451)2,430none — median 0.4493 (q25–q75 0.3618–0.5379)2,438none — median 0.4436 (q25–q75 0.3566–0.5321)2,446none — median 0.4383 (q25–q75 0.3517–0.5268)2,456none — median 0.4323 (q25–q75 0.3458–0.5201)2,464none — median 0.428 (q25–q75 0.3419–0.5163)2,472none — median 0.4242 (q25–q75 0.3381–0.5122)2,480none — median 0.4206 (q25–q75 0.3344–0.5086)2,490none — median 0.4162 (q25–q75 0.3296–0.504)2,498none — median 0.4128 (q25–q75 0.3266–0.5008)2,506none — median 0.4098 (q25–q75 0.3236–0.4981)2,516none — median 0.4068 (q25–q75 0.3201–0.4954)2,524none — median 0.405 (q25–q75 0.3184–0.4935)2,532none — median 0.4034 (q25–q75 0.3174–0.4921)2,542none — median 0.4019 (q25–q75 0.3165–0.4905)2,550none — median 0.4008 (q25–q75 0.3154–0.4898)

Sampling

How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide

Wavelengths
?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis.
601
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
1,350–2,550 none
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
2 none
Grid
?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives.
uniform
Observations
?Exported spectral rows in this source, including repeated measurements when present.
8,151

Signal & quality

Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide

Value range
?Minimum and maximum recorded spectral values, in the original signal representation.
0.0721 – 1.14
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.41 – 0.587
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.5144
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
617.4
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.1766
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
2.4208e-05
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
2.1e+04
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
9e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.177
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.0001837
Saturated
?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits.
0.0%
X-outliers
?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically.
4,325

Integrity & artefacts

Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide

NaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage.
0.00%
Inf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export.
0
Zero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing.
0.00%
Spike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged.
157,716
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
3.23%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
18,240
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
0.37%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide

Baseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit.
-0.027775
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0001613
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0012429
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.09446
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.26338
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.039886
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.10924
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.35508
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.71961
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.04626
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
2

Outliers & repeatability

Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide

PCA Q p95/median
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median.
6.9
Hotelling T2 p95/median
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median.
8.1
Mahalanobis H p95/median
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra.
2.8
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
2,105
RMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units.
0.010645
SAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians.
0.0046541
CV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal.
0.030335

Dimensionality (PCA)

Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide

Effective rank
?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions.
1.3
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
1
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
2
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
100.0%
Computed metric scores 30highest alert 1.00

Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/masked cell count (owner report)
?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.zero_ratio		0%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectance
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.mean_reflectance		0.514350.30weakToo darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curve
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.area_under_curve		617.390.30weakNormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.peak_to_peak		0.176630.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.variance		0.0207280.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxy
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_sigma_proxy		2.4208e-050.00weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_signal_to_noise_proxy		212480.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxy
?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.bandwise_roughness_signal_to_noise_proxy_min		2069.70.00weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_count		157,7161.00strongArtifactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_rate		3.23%1.00strongSuspicious spectrumInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_count		18,2400.37weakContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_rate		0.373%0.37weakNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.clip_fraction		4.08e-05%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.baseline_slope		-0.0277750.11weakStableIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.curvature_rms		0.00016130.03weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.d1_rms		0.00124290.05weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.pca_q_ratio		6.91090.86strongAtypical spectrumArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.hotelling_t2_ratio		8.07021.00strongExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis H
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.mahalanobis_h_ratio		2.84080.71mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrum
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.rms_to_mean_spectrum_p95		0.263381.00strongDifferent spectrumDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.sam_to_mean_spectrum_p95		0.109240.31weakSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.rms_intra_id		0.0106450.21weakStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.sam_intra_id		0.00465410.03weakStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.cv_intra_id		0.0303350.12weakStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score density
?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.pca_score_density		33.0511.00strongSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)
?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.knn_distance_ratio_p95		4.44891.00strongIsolated spectrumRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest score
?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.isolation_forest_score_p95		0.588841.00strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source recovered_spectra: 800 of 8,151 source observations are plotted. PC1 captures 95.4% and PC2 3.7% of the reported spectral variance: 99.1% together; 0.9% remains outside this view.

Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.

recovered_spectra PCA score plot-15-10-50510-6-4-202PC1 -2.804 · PC2 0.1873PC1 3.348 · PC2 0.1701PC1 -4.965 · PC2 -0.1395PC1 2.903 · PC2 0.5095PC1 -3.263 · PC2 -0.2373PC1 5.625 · PC2 0.3048PC1 -2.843 · PC2 -0.1236PC1 2.7 · PC2 0.5972PC1 4.791 · PC2 0.0007423PC1 2.547 · PC2 0.8172PC1 -2.357 · PC2 -0.8738PC1 0.874 · PC2 -0.8541PC1 -1.32 · PC2 -0.1349PC1 -3.476 · PC2 -0.7991PC1 -0.03244 · PC2 0.06263PC1 -1.521 · PC2 -0.9444PC1 -6.586 · PC2 0.6766PC1 -0.1639 · PC2 -0.6823PC1 1.959 · PC2 -0.528PC1 -7.663 · PC2 -0.317PC1 7.115 · PC2 0.5337PC1 1.919 · PC2 -0.001868PC1 -0.9271 · PC2 0.09059PC1 -0.8139 · PC2 0.003808PC1 -2.423 · PC2 -0.1031PC1 -3.88 · PC2 1.006PC1 -2.937 · PC2 -0.615PC1 -0.676 · PC2 -0.2419PC1 -0.7785 · PC2 0.1712PC1 -0.4374 · PC2 0.729PC1 0.267 · PC2 0.8261PC1 1.85 · PC2 -0.9421PC1 -4.771 · PC2 0.4946PC1 1.182 · PC2 -1.728PC1 -2.375 · PC2 0.3912PC1 -3.03 · PC2 -1.535PC1 0.9684 · PC2 -1.086PC1 2.961 · PC2 -0.03887PC1 -2.803 · PC2 -0.5021PC1 0.2092 · PC2 0.01965PC1 1.81 · PC2 0.6956PC1 0.4904 · PC2 0.3496PC1 -1.367 · PC2 -0.5079PC1 -1.087 · PC2 -1.345PC1 -3.731 · PC2 0.5775PC1 -3.957 · PC2 0.3033PC1 2.308 · PC2 -0.3678PC1 2.384 · PC2 0.6136PC1 -5.861 · PC2 0.3831PC1 4.961 · PC2 0.04494PC1 3.069 · PC2 0.6277PC1 -1.565 · PC2 1.007PC1 -2.278 · PC2 -0.1075PC1 -2.622 · PC2 -1.562PC1 -0.8279 · PC2 0.7065PC1 3.888 · PC2 -0.2236PC1 2.146 · PC2 -0.6513PC1 1.165 · PC2 0.3741PC1 -2.314 · PC2 0.2276PC1 -6.043 · PC2 0.2103PC1 0.0764 · PC2 0.5632PC1 -1.884 · PC2 0.1068PC1 -3.937 · PC2 -0.3519PC1 -3.859 · PC2 -0.1964PC1 -5.613 · PC2 0.7959PC1 -2.748 · PC2 0.299PC1 6.655 · PC2 -0.2593PC1 -3.83 · PC2 0.3317PC1 -3.797 · PC2 0.8053PC1 0.5692 · PC2 -0.2277PC1 -2.612 · PC2 0.2241PC1 0.2614 · PC2 -0.3853PC1 2.458 · PC2 -0.00635PC1 3.015 · PC2 0.1981PC1 -4.883 · PC2 0.01599PC1 -2.899 · PC2 0.01369PC1 1.381 · PC2 0.7163PC1 4.265 · PC2 0.4711PC1 5.976 · PC2 0.5643PC1 -3.169 · PC2 -0.704PC1 2.606 · PC2 0.2769PC1 3.003 · PC2 0.4344PC1 0.7447 · PC2 0.6311PC1 0.7889 · PC2 -0.005442PC1 6.659 · PC2 -0.06958PC1 -2.819 · PC2 0.05518PC1 -1.642 · PC2 0.434PC1 -4.982 · PC2 0.5008PC1 -0.2201 · PC2 -0.391PC1 -1.668 · PC2 0.3026PC1 0.1974 · PC2 0.232PC1 0.2085 · PC2 0.5753PC1 0.1801 · PC2 -0.2629PC1 -1.957 · PC2 0.427PC1 0.8287 · PC2 0.3891PC1 0.9164 · PC2 0.07562PC1 -2.013 · PC2 -0.06405PC1 -1.528 · PC2 -0.09484PC1 0.3681 · PC2 -0.2877PC1 -7.381 · PC2 -0.2352PC1 -0.2543 · PC2 0.05565PC1 -3.439 · PC2 0.3367PC1 -0.2124 · PC2 -0.2692PC1 2.405 · PC2 -0.1594PC1 -3.697 · PC2 -0.5564PC1 7.177 · PC2 0.4912PC1 0.6901 · PC2 -0.4079PC1 -1.069 · PC2 -0.3511PC1 2.586 · PC2 0.06416PC1 -7.727 · PC2 0.8468PC1 -0.758 · PC2 0.4702PC1 1.188 · PC2 0.2472PC1 1.224 · PC2 0.1332PC1 3.491 · PC2 -0.03509PC1 -2.377 · PC2 0.0882PC1 2.636 · PC2 0.01431PC1 2.049 · PC2 0.01808PC1 2.087 · PC2 0.06256PC1 1.804 · PC2 0.1346PC1 -2.134 · PC2 0.04022PC1 -4.15 · PC2 -0.4298PC1 2.549 · PC2 0.4259PC1 3.606 · PC2 0.4135PC1 -2.279 · PC2 -0.07397PC1 -3.164 · PC2 0.9077PC1 -1.43 · PC2 0.06493PC1 0.485 · PC2 0.05409PC1 0.2421 · PC2 0.6826PC1 -2.121 · PC2 0.6457PC1 3.593 · PC2 0.673PC1 -4.695 · PC2 0.1033PC1 -6.227 · PC2 0.7901PC1 1.604 · PC2 -0.539PC1 -2.621 · PC2 0.5928PC1 -1.524 · PC2 0.5306PC1 -1.663 · PC2 0.5867PC1 -2.228 · PC2 0.4212PC1 -2.377 · PC2 0.371PC1 -2.802 · PC2 -0.321PC1 -1.713 · PC2 0.2999PC1 -0.8004 · PC2 0.3559PC1 -6.426 · PC2 0.6009PC1 -7.408 · PC2 0.4261PC1 -3.667 · PC2 0.1959PC1 -2.946 · PC2 0.2814PC1 -3.667 · PC2 0.1959PC1 0.4664 · PC2 0.6548PC1 0.1801 · PC2 0.8945PC1 2.134 · PC2 -1.505PC1 3.141 · PC2 -0.6478PC1 -2.091 · PC2 -1.431PC1 1.412 · PC2 -0.4669PC1 -5.021 · PC2 0.6369PC1 -4.391 · PC2 0.7776PC1 0.06552 · PC2 0.1691PC1 -3.699 · PC2 -0.3658PC1 0.5794 · PC2 -0.3549PC1 6.407 · PC2 -0.0492PC1 4.406 · PC2 0.298PC1 -8.54 · PC2 1.224PC1 -4.053 · PC2 0.5281PC1 3.465 · PC2 -1.555PC1 4.979 · PC2 0.123PC1 -1.029 · PC2 0.293PC1 -3.231 · PC2 0.03587PC1 -1.583 · PC2 0.2517PC1 -3.568 · PC2 0.09535PC1 -0.2746 · PC2 -0.8833PC1 2.763 · PC2 -0.5597PC1 2.367 · PC2 -0.02537PC1 0.4976 · PC2 0.6654PC1 -6.612 · PC2 -1.818PC1 1.252 · PC2 -1.096PC1 -0.1729 · PC2 -0.4638PC1 1.082 · PC2 -1.554PC1 -4.257 · PC2 0.1706PC1 -5.954 · PC2 -0.3427PC1 -0.9066 · PC2 -0.2163PC1 -4.061 · PC2 0.5263PC1 1.442 · PC2 -0.2619PC1 -4.491 · PC2 -0.0006732PC1 -2.275 · PC2 0.8874PC1 5.608 · PC2 1.077PC1 1.409 · PC2 0.5427PC1 0.8331 · PC2 0.5594PC1 1.664 · PC2 0.3885PC1 0.6548 · PC2 -0.4748PC1 3.77 · PC2 -0.2338PC1 3.368 · PC2 -0.7261PC1 -3.182 · PC2 0.5706PC1 -5.78 · PC2 0.4143PC1 1.379 · PC2 0.03747PC1 0.2089 · PC2 0.2498PC1 2.925 · PC2 -0.6037PC1 -1.475 · PC2 0.304PC1 1.086 · PC2 -0.1186PC1 -3.289 · PC2 -0.2688PC1 -0.388 · PC2 -0.3258PC1 -2.818 · PC2 -0.7639PC1 -3.642 · PC2 -0.8641PC1 -6.916 · PC2 -0.07415PC1 -1.716 · PC2 0.1166PC1 -6.635 · PC2 0.2396PC1 -0.3523 · PC2 0.06566PC1 -5.649 · PC2 0.02672PC1 -5.175 · PC2 0.5717PC1 -1.1 · PC2 -0.123PC1 -2.875 · PC2 -0.3928PC1 1.695 · PC2 -0.09613PC1 3.931 · PC2 -0.3367PC1 8.342 · PC2 0.5039PC1 4.957 · PC2 0.556PC1 3.954 · PC2 -0.01796PC1 3.498 · PC2 0.01605PC1 -0.9596 · PC2 -0.2682PC1 2.059 · PC2 0.006528PC1 0.6986 · PC2 0.3034PC1 3.304 · PC2 0.3188PC1 -5.313 · PC2 -0.1532PC1 -7.359 · PC2 0.3456PC1 2.173 · PC2 -0.1526PC1 -4.512 · PC2 0.3167PC1 2.296 · PC2 0.3383PC1 -3.868 · PC2 0.8607PC1 -3.943 · PC2 0.7523PC1 3.717 · PC2 0.8699PC1 2.416 · PC2 -0.07193PC1 4.804 · PC2 -0.9355PC1 2.031 · PC2 0.06884PC1 -1.796 · PC2 0.3675PC1 2.478 · PC2 0.3455PC1 0.5783 · PC2 0.5234PC1 4.634 · PC2 0.4129PC1 -1.729 · PC2 -0.4009PC1 -1.216 · PC2 -1.241PC1 3.695 · PC2 0.8029PC1 -0.005781 · PC2 0.8147PC1 4.486 · PC2 -0.1062PC1 -7.056 · PC2 -2.531PC1 -0.6476 · PC2 0.2176PC1 0.884 · PC2 0.1622PC1 -2.963 · PC2 0.5372PC1 -0.8076 · PC2 0.0751PC1 -1.948 · PC2 0.5484PC1 -3.82 · PC2 0.2183PC1 -2.532 · PC2 -0.6468PC1 1.368 · PC2 0.126PC1 1.743 · PC2 -0.2711PC1 -2.996 · PC2 -0.534PC1 -3.256 · PC2 0.2449PC1 -0.6308 · PC2 -0.7193PC1 -0.4527 · PC2 -0.4406PC1 -1.538 · PC2 -1.15PC1 -1.423 · PC2 0.4259PC1 -2.444 · PC2 -0.03024PC1 3.087 · PC2 -0.5898PC1 0.04213 · PC2 0.6006PC1 0.3918 · PC2 0.6013PC1 1.084 · PC2 0.3843PC1 -0.3036 · PC2 -2.182PC1 -7.448 · PC2 -0.7733PC1 -2.027 · PC2 -0.9297PC1 -1.117 · PC2 0.2531PC1 -3.83 · PC2 -0.5255PC1 1.813 · PC2 -1.206PC1 -1.14 · PC2 -0.4605PC1 -1.606 · PC2 -0.07431PC1 0.5844 · PC2 0.04895PC1 0.4724 · PC2 -0.4813PC1 -4.992 · PC2 -0.3796PC1 0.9317 · PC2 -0.5978PC1 -3.569 · PC2 -1.329PC1 0.5865 · PC2 0.4624PC1 -1.341 · PC2 -2.517PC1 -6.706 · PC2 1.017PC1 -1.973 · PC2 0.6434PC1 -1.506 · PC2 -0.7881PC1 4.523 · PC2 0.0009352PC1 -4.947 · PC2 0.6572PC1 -0.5429 · PC2 -0.5124PC1 1.834 · PC2 -0.696PC1 -2.274 · PC2 -0.1694PC1 2.743 · PC2 -0.1007PC1 -3.583 · PC2 0.2455PC1 -0.7503 · PC2 -1.091PC1 0.127 · PC2 0.3714PC1 -1.085 · PC2 -0.2252PC1 -1.492 · PC2 -1.183PC1 1.208 · PC2 0.8623PC1 -2.719 · PC2 -0.89PC1 1.169 · PC2 0.6167PC1 2.281 · PC2 -0.1338PC1 0.2133 · PC2 0.6211PC1 -2.136 · PC2 0.1663PC1 -3.76 · PC2 -1.299PC1 -6.809 · PC2 0.9131PC1 6.603 · PC2 0.3152PC1 -4.013 · PC2 0.3195PC1 7.209 · PC2 -0.2292PC1 -3.821 · PC2 0.1458PC1 -1.096 · PC2 1.043PC1 5.118 · PC2 0.2966PC1 -2.142 · PC2 -0.1474PC1 3.099 · PC2 0.8771PC1 0.1169 · PC2 -0.2827PC1 -0.3894 · PC2 -0.4763PC1 0.9649 · PC2 -1.142PC1 4.255 · PC2 -1.288PC1 0.9858 · PC2 -0.4875PC1 -5.567 · PC2 0.768PC1 -7.554 · PC2 -0.2049PC1 0.2459 · PC2 0.4917PC1 1.522 · PC2 1.008PC1 -4.077 · PC2 0.5035PC1 -0.6134 · PC2 0.2557PC1 -0.4359 · PC2 -2.138PC1 6.18 · PC2 0.7185PC1 2.729 · PC2 0.727PC1 -0.05862 · PC2 -1.279PC1 0.1388 · PC2 -0.3093PC1 4.911 · PC2 0.358PC1 3.042 · PC2 -0.3857PC1 3.117 · PC2 -0.3744PC1 -0.1662 · PC2 -0.2031PC1 -3.28 · PC2 -0.1696PC1 0.4015 · PC2 -0.8119PC1 4.963 · PC2 0.2112PC1 4.831 · PC2 0.4399PC1 4.24 · PC2 -0.09898PC1 -0.1222 · PC2 -0.06703PC1 -3.767 · PC2 -0.1666PC1 -2.791 · PC2 0.1362PC1 -0.5244 · PC2 -1.609PC1 -2.268 · PC2 -0.4422PC1 -3.641 · PC2 -1.715PC1 6.038 · PC2 0.2146PC1 6.514 · PC2 0.3243PC1 -2.787 · PC2 0.4095PC1 4.392 · PC2 0.2673PC1 -0.7719 · PC2 -0.727PC1 5.772 · PC2 0.4019PC1 3.258 · PC2 -0.3362PC1 -2.859 · PC2 0.467PC1 5.64 · PC2 1.138PC1 0.6343 · PC2 -0.41PC1 2.738 · PC2 0.2092PC1 0.664 · PC2 0.7683PC1 6.125 · PC2 -0.4716PC1 6.491 · PC2 -0.0065PC1 4.733 · PC2 -0.2624PC1 5.351 · PC2 -0.3879PC1 1.056 · PC2 0.2021PC1 0.6156 · PC2 0.4504PC1 -3.089 · PC2 1.17PC1 0.7539 · PC2 0.1264PC1 -5.085 · PC2 -0.05482PC1 -0.1003 · PC2 -0.2467PC1 -0.8409 · PC2 0.02332PC1 -3.781 · PC2 -1.32PC1 -0.3557 · PC2 0.111PC1 3.435 · PC2 -0.3656PC1 3.54 · PC2 0.2044PC1 2.263 · PC2 0.4537PC1 1.564 · PC2 0.5327PC1 -2.749 · PC2 -0.4795PC1 -0.01439 · PC2 -0.5761PC1 -4.433 · PC2 -0.6865PC1 4.673 · PC2 -0.05805PC1 0.06372 · PC2 -0.09013PC1 0.5349 · PC2 -0.3588PC1 0.7025 · PC2 -0.3129PC1 0.6602 · PC2 -0.1363PC1 1.672 · PC2 -0.3583PC1 2.025 · PC2 0.05266PC1 -1.308 · PC2 0.2755PC1 3.392 · PC2 0.3255PC1 -2.672 · PC2 1.146PC1 -4.438 · PC2 1.166PC1 0.08666 · PC2 -0.0505PC1 4.902 · PC2 0.2878PC1 -4.292 · PC2 -0.08101PC1 -0.4723 · PC2 0.3938PC1 5.51 · PC2 0.3486PC1 1.899 · PC2 -0.3984PC1 4.666 · PC2 -0.427PC1 1.994 · PC2 0.2786PC1 6.468 · PC2 0.123PC1 -6.198 · PC2 0.9578PC1 -1.671 · PC2 -0.16PC1 -0.03261 · PC2 -0.4403PC1 0.3087 · PC2 0.2184PC1 -1.051 · PC2 0.6379PC1 -2.569 · PC2 0.5317PC1 -1.35 · PC2 0.2956PC1 -2.115 · PC2 -0.1177PC1 -10.15 · PC2 0.7817PC1 1.83 · PC2 0.4351PC1 -1.852 · PC2 -0.3741PC1 -0.07048 · PC2 -3.961PC1 -1.497 · PC2 0.278PC1 0.8157 · PC2 0.6203PC1 0.3385 · PC2 0.473PC1 1.314 · PC2 0.8525PC1 -2.073 · PC2 0.4941PC1 -0.1854 · PC2 0.6625PC1 1.451 · PC2 0.6203PC1 1.206 · PC2 0.6285PC1 4.804 · PC2 0.476PC1 -7.015 · PC2 -0.6542PC1 -3.952 · PC2 0.7314PC1 1.277 · PC2 0.1036PC1 1.412 · PC2 0.2933PC1 4.918 · PC2 -0.178PC1 7.758 · PC2 0.3678PC1 -2.172 · PC2 -0.1346PC1 -1.209 · PC2 -1.852PC1 -2.839 · PC2 0.009859PC1 0.9039 · PC2 0.4945PC1 7.585 · PC2 0.6183PC1 -2.723 · PC2 -0.2351PC1 2.586 · PC2 -0.5555PC1 0.7561 · PC2 0.4488PC1 0.5926 · PC2 -0.6466PC1 2.079 · PC2 -0.1449PC1 -0.8276 · PC2 0.768PC1 -3.899 · PC2 0.3374PC1 -1.135 · PC2 0.5552PC1 2.009 · PC2 -0.2275PC1 -7.228 · PC2 -0.8778PC1 -3.863 · PC2 -0.1521PC1 -6.748 · PC2 1.116PC1 -3.354 · PC2 0.5033PC1 -4.039 · PC2 -1.699PC1 6.497 · PC2 0.1894PC1 -5.514 · PC2 -0.276PC1 3.193 · PC2 -0.5727PC1 -2.978 · PC2 -0.3222PC1 2.333 · PC2 -0.3328PC1 0.5184 · PC2 0.4422PC1 -3.035 · PC2 0.6469PC1 5.981 · PC2 0.3917PC1 6.172 · PC2 0.4407PC1 -4.284 · PC2 0.3486PC1 3.348 · PC2 0.6413PC1 -5.465 · PC2 -0.4261PC1 3.39 · PC2 -0.8399PC1 -1.17 · PC2 -4.264PC1 -2.111 · PC2 -0.3895PC1 4.505 · PC2 -0.5633PC1 3.207 · PC2 -0.3344PC1 -0.4622 · PC2 -0.0555PC1 3.928 · PC2 -0.5503PC1 0.9345 · PC2 -0.4334PC1 4.063 · PC2 -0.31PC1 0.883 · PC2 0.1535PC1 -0.6468 · PC2 0.5262PC1 -3.523 · PC2 0.2636PC1 3.718 · PC2 0.4606PC1 -1.09 · PC2 0.8571PC1 -3.369 · PC2 1.136PC1 -0.8387 · PC2 -0.03118PC1 0.02742 · PC2 -0.202PC1 -6.573 · PC2 0.05199PC1 -3.134 · PC2 0.3059PC1 -5.18 · PC2 -0.1265PC1 -0.05958 · PC2 -0.06234PC1 -0.2755 · PC2 -0.1469PC1 0.5896 · PC2 -0.5615PC1 3.176 · PC2 -0.02855PC1 1.674 · PC2 -0.3781PC1 3.019 · PC2 0.1787PC1 3.279 · PC2 0.4126PC1 -7.168 · PC2 0.4305PC1 -5.508 · PC2 0.8873PC1 1.669 · PC2 0.4116PC1 2.184 · PC2 0.3442PC1 -3.99 · PC2 0.8641PC1 5.282 · PC2 0.2655PC1 3.242 · PC2 -0.2537PC1 2.715 · PC2 -0.01852PC1 2.413 · PC2 -0.04204PC1 2.267 · PC2 -0.03354PC1 3.961 · PC2 0.3966PC1 -3.783 · PC2 1.066PC1 -4.181 · PC2 -0.251PC1 -0.898 · PC2 0.2105PC1 3.323 · PC2 -0.1568PC1 0.5644 · PC2 0.003961PC1 1.061 · PC2 -0.5818PC1 -0.06417 · PC2 0.09156PC1 2.229 · PC2 -0.05324PC1 -0.8371 · PC2 -1.213PC1 6.402 · PC2 0.7331PC1 -2.591 · PC2 0.6249PC1 -2.727 · PC2 0.3368PC1 -2.725 · PC2 0.4528PC1 -1.609 · PC2 0.4264PC1 -1.784 · PC2 0.5257PC1 -1.531 · PC2 0.4394PC1 -5.703 · PC2 0.9845PC1 1.943 · PC2 -0.14PC1 -0.4715 · PC2 0.2978PC1 0.617 · PC2 0.1785PC1 -0.8232 · PC2 0.187PC1 0.5881 · PC2 -0.5571PC1 1.002 · PC2 -0.6316PC1 3.345 · PC2 0.2479PC1 -1.015 · PC2 0.3612PC1 -1.414 · PC2 0.2205PC1 1.543 · PC2 0.7134PC1 2.46 · PC2 0.1134PC1 3.975 · PC2 0.4484PC1 3.273 · PC2 -1.074PC1 -1.05 · PC2 -0.8342PC1 4.255 · PC2 -0.4698PC1 3.298 · PC2 -1.114PC1 -4.491 · PC2 -0.1853PC1 1.784 · PC2 0.4131PC1 -0.9009 · PC2 -0.4806PC1 2.577 · PC2 -0.8873PC1 -0.9244 · PC2 -0.0722PC1 -1.282 · PC2 -1.818PC1 5.824 · PC2 0.07206PC1 -2.91 · PC2 -0.9533PC1 4.012 · PC2 0.6838PC1 -0.9151 · PC2 -0.3234PC1 1.95 · PC2 0.5018PC1 -3.314 · PC2 -0.2744PC1 3.497 · PC2 -0.17PC1 4.245 · PC2 0.3079PC1 -0.4541 · PC2 0.747PC1 -0.2617 · PC2 -1.004PC1 0.0865 · PC2 -0.02551PC1 -0.3582 · PC2 -0.532PC1 -1.714 · PC2 -0.6648PC1 5.103 · PC2 0.08522PC1 1.123 · PC2 0.07622PC1 1.467 · PC2 0.3795PC1 -0.1848 · PC2 0.03353PC1 -2.768 · PC2 -0.276PC1 -2.439 · PC2 0.1611PC1 1.148 · PC2 0.8015PC1 -2.534 · PC2 -1.121PC1 -2.032 · PC2 0.07968PC1 1.421 · PC2 -0.5131PC1 -0.8107 · PC2 -0.5586PC1 2.435 · PC2 -0.3635PC1 5.773 · PC2 0.1979PC1 -1.996 · PC2 -2.123PC1 1.943 · PC2 0.4646PC1 -2.967 · PC2 0.6499PC1 -3.687 · PC2 0.5524PC1 2.145 · PC2 0.6959PC1 6.431 · PC2 0.4544PC1 -2.645 · PC2 0.4539PC1 4.308 · PC2 0.304PC1 6.083 · PC2 0.4053PC1 1.852 · PC2 0.6386PC1 7 · PC2 -0.5327PC1 3.455 · PC2 -0.1089PC1 -2.926 · PC2 0.6242PC1 -4.005 · PC2 0.2803PC1 0.3303 · PC2 -0.2559PC1 1.104 · PC2 0.7777PC1 -0.4239 · PC2 0.1262PC1 -4.663 · PC2 0.457PC1 -6.177 · PC2 0.1324PC1 -0.4752 · PC2 0.03566PC1 -2.218 · PC2 -0.5963PC1 0.5845 · PC2 -0.02503PC1 2.289 · PC2 0.005949PC1 5.742 · PC2 0.4926PC1 -0.5488 · PC2 0.3849PC1 5.958 · PC2 0.08196PC1 0.3241 · PC2 0.09913PC1 -7.53 · PC2 -0.1733PC1 -0.169 · PC2 0.5949PC1 -0.9587 · PC2 0.717PC1 4.72 · PC2 -0.4031PC1 -3.137 · PC2 -0.1008PC1 2.737 · PC2 -0.3975PC1 -1.287 · PC2 0.2655PC1 -1.083 · PC2 -0.02861PC1 -1.672 · PC2 0.6404PC1 -1.093 · PC2 0.4591PC1 -1.325 · PC2 0.6238PC1 -1.388 · PC2 0.5114PC1 -1.844 · PC2 0.5103PC1 0.06249 · PC2 0.3986PC1 0.6131 · PC2 0.133PC1 -2.441 · PC2 -0.5534PC1 0.1123 · PC2 -0.8615PC1 1.31 · PC2 -0.9597PC1 -6.329 · PC2 0.3327PC1 -1.801 · PC2 -0.4561PC1 0.7441 · PC2 0.3586PC1 2.289 · PC2 0.2802PC1 0.8003 · PC2 0.4057PC1 0.1096 · PC2 0.647PC1 -0.3336 · PC2 0.4589PC1 2.925 · PC2 0.6001PC1 -0.4443 · PC2 -1.965PC1 -1.097 · PC2 -0.8351PC1 3.351 · PC2 -0.9803PC1 -1.953 · PC2 -0.703PC1 3.351 · PC2 -0.4368PC1 -1.787 · PC2 -1.627PC1 6.595 · PC2 -0.07372PC1 6.728 · PC2 0.3632PC1 -3.392 · PC2 0.1739PC1 -3.841 · PC2 0.5357PC1 1.806 · PC2 -0.9969PC1 3.288 · PC2 -0.9942PC1 -3.271 · PC2 -0.6346PC1 2.316 · PC2 -0.6242PC1 0.3661 · PC2 -0.2805PC1 -1.746 · PC2 -0.4436PC1 -0.5073 · PC2 0.2257PC1 -1.547 · PC2 -0.4751PC1 -1.948 · PC2 -0.5733PC1 -1.181 · PC2 0.4119PC1 5.495 · PC2 0.8022PC1 2.366 · PC2 0.8305PC1 2.908 · PC2 0.8923PC1 -0.6246 · PC2 0.6846PC1 1.396 · PC2 -0.2879PC1 -5.971 · PC2 0.2995PC1 -2.048 · PC2 -0.2549PC1 -2.657 · PC2 0.6298PC1 4.018 · PC2 -0.04944PC1 -3.472 · PC2 0.1732PC1 0.8106 · PC2 0.004691PC1 8.182 · PC2 0.1327PC1 0.5862 · PC2 -0.1511PC1 0.6167 · PC2 0.04981PC1 -3.086 · PC2 0.1607PC1 3.595 · PC2 0.5095PC1 8.422 · PC2 0.4914PC1 -1.451 · PC2 -0.1417PC1 1.691 · PC2 0.7044PC1 0.3548 · PC2 -0.1391PC1 -0.2621 · PC2 0.06973PC1 3.065 · PC2 0.3184PC1 4.253 · PC2 0.4662PC1 3.541 · PC2 -0.1584PC1 -2.124 · PC2 0.3293PC1 -1.902 · PC2 -0.4044PC1 -4.899 · PC2 0.9809PC1 -4.86 · PC2 1.109PC1 -0.4356 · PC2 -0.6459PC1 -1.948 · PC2 -0.4162PC1 2.86 · PC2 0.6309PC1 1.835 · PC2 0.1766PC1 -2.951 · PC2 0.3638PC1 3.525 · PC2 0.3026PC1 -5.327 · PC2 0.4792PC1 -0.345 · PC2 -0.09045PC1 5.205 · PC2 0.7734PC1 0.3376 · PC2 0.2453PC1 1.733 · PC2 0.5621PC1 1.36 · PC2 0.5414PC1 3.7 · PC2 0.1564PC1 -0.05515 · PC2 0.01413PC1 -0.9997 · PC2 0.3055PC1 2.976 · PC2 -0.2324PC1 0.9061 · PC2 0.05053PC1 3.487 · PC2 0.315PC1 -0.1345 · PC2 -0.7329PC1 3.223 · PC2 0.242PC1 -1.549 · PC2 -0.4654PC1 -2.57 · PC2 -0.4062PC1 1.457 · PC2 0.2515PC1 2.757 · PC2 0.3423PC1 3.981 · PC2 0.5429PC1 -0.2536 · PC2 -0.1544PC1 2.49 · PC2 -1.327PC1 -0.5114 · PC2 -0.8466PC1 -1.493 · PC2 0.1277PC1 0.7684 · PC2 -0.443PC1 1.656 · PC2 -0.2501PC1 2.483 · PC2 -0.7392PC1 6.196 · PC2 0.1257PC1 4.077 · PC2 -0.1652PC1 3.362 · PC2 0.3892PC1 -4.697 · PC2 0.6729PC1 0.2515 · PC2 0.6169PC1 -2.206 · PC2 -0.9146PC1 -0.6496 · PC2 -1.458PC1 -0.1706 · PC2 0.1393PC1 -4.121 · PC2 0.2586PC1 3.897 · PC2 0.4487PC1 -4.943 · PC2 0.4994PC1 2.865 · PC2 0.5017PC1 0.5952 · PC2 0.7846PC1 3.848 · PC2 0.8945PC1 4.825 · PC2 -0.4664PC1 -1.421 · PC2 -0.3708PC1 -2.617 · PC2 -2.139PC1 -1.278 · PC2 0.2907PC1 0.1648 · PC2 -1.119PC1 1.392 · PC2 -1.039PC1 -3.275 · PC2 -1.004PC1 0.02468 · PC2 -0.1865PC1 4.568 · PC2 -0.003664PC1 1.922 · PC2 0.1476PC1 0.4873 · PC2 -1.16PC1 -1.279 · PC2 -0.1216PC1 2.626 · PC2 -0.853PC1 1.355 · PC2 0.1356PC1 -0.9612 · PC2 -0.4741PC1 -0.1339 · PC2 0.1278PC1 2.974 · PC2 -1.465PC1 -6.668 · PC2 0.23PC1 -3.839 · PC2 -0.411PC1 -3.951 · PC2 0.4611PC1 -4.59 · PC2 -2.016PC1 -0.2127 · PC2 -0.5145PC1 1.053 · PC2 -0.8029PC1 -0.4888 · PC2 -0.8171PC1 3.891 · PC2 -0.8435PC1 3.834 · PC2 -0.6883PC1 -1.219 · PC2 -0.7932PC1 -1.954 · PC2 0.06187PC1 -1.735 · PC2 -0.6315PC1 0.2785 · PC2 0.3006PC1 2.076 · PC2 -0.9628PC1 0.4204 · PC2 0.4245PC1 -1.804 · PC2 0.3578PC1 4.095 · PC2 0.5286PC1 -2.256 · PC2 0.2685PC1 0.5957 · PC2 0.5153PC1 -5.233 · PC2 0.3322PC1 3.614 · PC2 -0.01663PC1 1.729 · PC2 0.2592PC1 0.5084 · PC2 0.3572PC1 3.373 · PC2 -0.4377PC1 -1.631 · PC2 0.2348PC1 3.605 · PC2 0.7142PC1 3.565 · PC2 0.6336PC1 -2.847 · PC2 0.761PC1 4.549 · PC2 0.1988PC1 4.799 · PC2 -0.6626PC1 3.683 · PC2 -0.4625PC1 -0.9663 · PC2 0.2473PC1 2.969 · PC2 -0.3029PC1 3.345 · PC2 -2.058PC1 4.235 · PC2 -0.01321PC1 3.644 · PC2 0.5773PC1 2.591 · PC2 -0.006343PC1 2.426 · PC2 -0.6006PC1 3.375 · PC2 0.6749PC1 2.91 · PC2 -0.2288PC1 4.243 · PC2 0.1911PC1 -1.585 · PC2 0.1583PC1 1.584 · PC2 0.5381PC1 -1.183 · PC2 -0.7137PC1 4.666 · PC2 0.1224PC1 -2.009 · PC2 0.4755PC1 -6 · PC2 0.4052PC1 -0.8635 · PC2 0.6744PC1 4.651 · PC2 0.6334PC1 -1.219 · PC2 0.5879PC1 2.545 · PC2 0.2689PC1 0.603 · PC2 0.09733PC1 0.5756 · PC2 0.05883PC1 -0.1772 · PC2 0.1803PC1 0.9432 · PC2 -0.2885PC1 5.755 · PC2 0.2106PC1 1.738 · PC2 0.154PC1 2.86 · PC2 -0.06307PC1 1.285 · PC2 0.005988PC1 3.761 · PC2 0.1828PC1 -1.29 · PC2 0.05661PC1 -1.968 · PC2 0.02662PC1 -2.536 · PC2 -0.1853PC1 0.9945 · PC2 -0.11PC1 -5.727 · PC2 0.8454PC1 4.491 · PC2 0.3297PC1 2.243 · PC2 -0.4455PC1 0.5738 · PC2 -0.04643PC1 0.7432 · PC2 -1.323PC1 -1.102 · PC2 -0.3225PC1 1.927 · PC2 0.4975PC1 3.462 · PC2 0.4352PC1 0.3937 · PC2 0.2426PC1 -6.182 · PC2 1.155PC1 -2.973 · PC2 0.8249PC1 1.049 · PC2 0.65PC1 -0.6413 · PC2 0.7156PC1 -4.058 · PC2 0.2596PC1 1.18 · PC2 0.5554PC1 3.641 · PC2 0.2273PC1 -1.881 · PC2 0.9656PC1 -1.471 · PC2 0.8708PC1 -4.385 · PC2 0.7988PC1 -7.962 · PC2 0.6814PC1 1.516 · PC2 0.7559PC1 -0.6023 · PC2 0.339PC1 -0.7929 · PC2 0.2618PC1 -0.9729 · PC2 -0.3616PC1 (95.4%)PC2 (3.7%)800 scores
View plotted PCA coordinates

Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
1-2.80450.18731
23.34760.1701
3-4.965-0.13954
42.90250.50955
5-3.2628-0.23727
65.62510.3048
7-2.8434-0.12364
82.69960.59715
94.79130.0007423
102.54750.81722
11-2.3573-0.87384
120.874-0.85411
13-1.3196-0.13493
14-3.4756-0.79911
15-0.0324420.062628
16-1.5214-0.94442
17-6.58610.67661
18-0.16389-0.68233
191.959-0.52801
20-7.6631-0.31703
217.11550.53366
221.9189-0.0018683
23-0.927080.090588
24-0.813870.0038077
25-2.4225-0.10314
26-3.87991.0061
27-2.9367-0.615
28-0.67603-0.2419
29-0.778520.17121
30-0.437450.72904
310.267040.82606
321.8504-0.94213
33-4.77070.49464
341.1822-1.7282
35-2.37460.39115
36-3.0305-1.5352
370.96841-1.0864
382.9605-0.038867
39-2.8034-0.50206
400.209170.019648
411.81030.69561
420.490360.34959
43-1.3665-0.50789
44-1.0871-1.3447
45-3.73130.57749
46-3.95690.30332
472.3079-0.3678
482.38360.61358
49-5.86120.38311
504.96060.04494
513.06940.62775
52-1.56521.0074
53-2.2781-0.10748
54-2.6223-1.562
55-0.827920.70647
563.8877-0.22358
572.1455-0.65127
581.16530.37411
59-2.31440.2276
60-6.04350.21026
610.07640.56323
62-1.88440.10681
63-3.9369-0.35193
64-3.8594-0.19643
65-5.61260.79593
66-2.74780.29897
676.6546-0.25927
68-3.83020.33171
69-3.79730.80528
700.56916-0.2277
71-2.61150.22412
720.26144-0.38529
732.4578-0.0063503
743.01540.19813
75-4.88350.015994
76-2.89910.013689
771.38110.71628
784.26490.47114
795.97620.56427
80-3.1689-0.70404
812.60640.27688
823.00260.43445
830.744720.63112
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7923.64150.22731
793-1.88110.96564
794-1.47060.87081
795-4.38530.79885
796-7.9620.68143
7971.51640.75595
798-0.602310.33904
799-0.792910.26178
800-0.97285-0.3616

PCA explained variance — how many dimensions?

Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.

PCA explained variance0%25%50%75%100%PC1: 95.1% (cumulative 95.1%)1PC2: 4.0% (cumulative 99.1%)2PC3: 0.5% (cumulative 99.5%)3PC4: 0.3% (cumulative 99.8%)4PC5: 0.1% (cumulative 99.9%)5PC6: 0.0% (cumulative 99.9%)6PC7: 0.0% (cumulative 100.0%)7PC8: 0.0% (cumulative 100.0%)8PC9: 0.0% (cumulative 100.0%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC195.06%
PC24.00%
PC30.46%
PC40.27%
PC50.08%
PC60.04%
PC70.03%
PC80.02%
PC90.01%
PC100.01%
X-Y spectral correlation 20

Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.

Spectral association with oc_usda_c729_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_nir · oc_usda_c729_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation1,0001,5002,0002,5003,000|r|signed raxis · Pearson correlation scale

Spectral association with c_tot_usda_a622_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_nir · c_tot_usda_a622_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation1,0001,5002,0002,5003,000|r|signed raxis · Pearson correlation scale

Spectral association with n_tot_usda_a623_w_pct

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X_nir · n_tot_usda_a623_w_pct spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation1,0001,5002,0002,5003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
oc_usda_c729_w_pct0.2652,1200.2330.0%
c_tot_usda_a622_w_pct0.2352,1200.210.0%
n_tot_usda_a623_w_pct0.31,3500.270.0%
s_tot_usda_a624_w_pct0.1631,9420.06870.0%
ph_h2o_usda_a268_index0.2621,9100.1180.0%
bd_usda_a4_g_cm30.3482,1240.320.0%
clay_tot_usda_a334_w_pct0.4282,5500.2470.0%
silt_tot_usda_c62_w_pct0.232,5360.1880.0%
sand_tot_usda_c60_w_pct0.05052,5500.02740.0%
caco3_usda_a54_w_pct0.2812,1920.240.0%
cec_usda_a723_cmolc_kg0.451,9140.3860.0%
ca_ext_usda_a722_cmolc_kg0.2591,9400.1120.0%
mg_ext_usda_a724_cmolc_kg0.2981,9100.2210.0%
k_ext_usda_a725_cmolc_kg0.2691,9120.2080.0%
na_ext_usda_a726_cmolc_kg0.09871,9820.08870.0%
wr_33kPa_usda_a415_w_pct0.3462,3620.3020.0%
wr_1500kPa_usda_a417_w_pct0.4342,5500.3430.0%
al_dith_usda_a65_w_pct0.1982,1740.140.0%
p_ext_usda_a652_mg_kg0.03952,5500.01090.0%
p_ext_usda_a1070_mg_kg0.371,3500.2810.0%

Metric interpretation reference

Definitions, formulas and possible explanations for the reported values. A possible cause requires verification against acquisition records. Reading guide

Metric catalog 30

Look up each measured quantity, its calculation and the heuristic alert mapping before interpreting a high or low value.

FamilyMetricWhat it measuresHigher values suggestLower values suggestPossible causesCalculation / score
Data integrityInvalid/masked cell count (owner report)Null or masked cells in the evaluated populationTo investigateComplete according to the declared maskMissing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratioMissing dataCorrupted spectrumFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf countInfinite valuesCorruptionNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratioNull columns or cellsTruncated spectrumNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectanceAverage levelToo light / background visibleToo darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curveOverall intensityIllumination differenceNormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)Dynamic rangeStrong variabilityFlat spectrumSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVarianceSpectral variabilityNormal or heterogeneousFlat spectrumBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxyHigh frequency noiseNoisyStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxySignal qualityGood signalBad signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxyLocalized noiseReliable zoneProblem areaDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike countNarrow peaksArtifactsClean spectrumCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike ratePeak densitySuspicious spectrumNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump countDiscontinuitiesDetector connectionContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rateJump frequencySpectral problemNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fractionSaturationClippingNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slopeOverall slopeDriftStableIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMSCurvatureUnusual shapeSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMSLocal variabilityStructured spectrumFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)Not explained by PCAAtypical spectrumCompliantArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²Extreme in PCAExtreme but consistentCentralNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis HDistance to cloudGlobal outlierNormal populationDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrumAverage distanceDifferent spectrumTypicalDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)Difference in shapeDifferent shapeSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-IDReproducibilityPoor repeatabilityStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-IDShape variationUnstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-IDInternal variabilityBad controlStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score densityClustersSubpopulationsHomogeneousDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)Local anomalyIsolated spectrumNormal populationRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest scoreGlobal anomalyAtypical spectrumNormalVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions

Find measurement-specific checks and acquisition context that can help interpret a source's spectral patterns.

TechnologyAdaptations / metricsTarget anomaliesPractical guidance
UV-Vis 300-1000 nmBaseline, overall slope, drift at edges 300-350 and 900-1000; metrics by zonesStray light, bad white, saturation, weak signal at the endsThe edges are often unstable; also calculate edge/middle scores.
UV-Vis 300-1000 nmSaturation / clipping near max absorbance or max reflectanceSignal clippedVery important if strong absorption.
UV-Vis 300-1000 nmRed-edge, peak position, band ratios for vegetationBiological shift or optical artifactHelps distinguish real change from acquisition problem.
UV-Vis 300-1000 nmSmoothness / roughness indexHigh frequency noiseOften more informative than the SNR alone.
MIR / ATR-FTIRATR contact quality index: overall intensity, total area, depth of key bandsPoor crystal-sample contactCrucial: many anomalies come from the ATR contact.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsBad atmospheric correctionFrequent spurious peaks.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, baseline driftVery useful before PCA.
MIR / ATR-FTIRPeak position shiftBad spectral alignment/calibrationImportant in FTIR because small shifts count.
MIR / ATR-FTIRBand area ratios on known bandsChemically incoherent spectrumTo be adapted by matrix: polysaccharides, proteins, lipids, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Weak injection, unstable ionizationMS equivalent of the global spectral level.
HS-MSNumber of peaks detectedPoor or too noisy spectrumToo little = bad signal; too much = noise/contamination.
HS-MSMass accuracy / m/z driftMass calibration problemFundamental in HRMS.
HS-MSRetention time drift if LC/GC-MSChromatographic driftMonitor on standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overCompare samples vs blanks.
HS-MSInternal standard CVInstrumental variabilityVery robust if standards available.
HS-MSMissingness by featureDetection instabilityCrucial for filtering variables.
With repetitionsIntra-sample RMSOverall repeatabilityApplicable to all technologies.
With repetitionsSAM / intra-sample correlationShape repeatabilityVery useful for spectra.
With repetitionsIntra-sample CV per band / featureLocal repeatabilityDetects unstable areas.
With repetitionsICC or variance componentsShare of sample vs. technical varianceVery useful if several repetitions per sample.
With repetitionsDistance to intra-ID centroidAberrant repetitionAllows you to flag the bad repetition rather than the entire sample.
Bug-hunting / supervised audits

Review suggested follow-up methods for suspected data issues and whether their results are available in this explorer.

Potential issue familySuggested methodsWhat it detectsExplorer availability
Global spectral shiftInter-dataset average spectrum correlation, DTW, cross-correlation, comparison of peak positionsWavelength shift, misalignment, different interpolationPartially calculated: cross-correlation lag and dispersion of peak positions vs. average spectrum.
Baseline / offset / gainRegression each spectrum vs average spectrum: x = a + b ref + residual; followed by a, b, residual RMSAdditive offset, multiplicative effect, baseline driftCalculated in reference.affine_*.
Row mixing / X-M-Y mismatchIndex check, row hash, ID duplication, intra-ID spectral distance, inconsistent labelsMixed up rows, misaligned metadata, Y assigned to wrong spectrumPartially covered by intra-ID repeatability; checks index/hash to add to the canonical pipeline.
Information leak / poorly split repetitionsGroupKFold by sample_id vs StratifiedKFold random; auditing partitions by sample_idArtificially good performance due to repetitionsRequires splits and benchmark model; not calculated by the descriptive card.
Label bugsClose samples in X but different Y, confident learning, systematic FP/FN errorsInverted Y, input errors, ambiguous classesRequires Y and/or model; recommended for supervised explorer.
Hidden subdomainsPCA/UMAP/t-SNE + unsupervised clustering + association with dataset/Y/date/operatorBatches, campaigns, probes, backgrounds not providedPartially calculated by PCA and distance ratio kNN (not LOF); UMAP/t-SNE outside the static card.
Unknown localized artifactsWavelength x dataset map: mean difference, variance difference, KS per wavelengthUnanticipated anomalous spectral regionsTo be calculated at the bank level when several datasets share a spectral axis.
Instrumental breaksDiscontinuities in derivatives, changepoint detectionSplice, detector connection, local jump not providedCalculated by jump/spike rates; more advanced changepoint to add.
Spectral mixing/contaminationNMF / unmixing / reconstruction by convex hullExternal component: background, plastic, soilNot calculated automatically; requires component assumptions or large library.
Unstable but predictive featuresModel importance vs. QC instability by variableModel that learns an artifact rather than a biological signalRequires supervised model; recommended for benchmark reporting.

Curated scientific variables

Scientific roles derived from the source contract. M denotes experimental metadata; Y denotes informative response variables. Reading guide

10 experimental metadata fields (M) · 23 informative targets (Y).

VariableRoleDistinct valuesCompleteRange / balance
scan_labM280.49%IR 1.594
instrument_modelM10100%IR 3.807
scan_model_codeM980.49%IR 2.181
sample_preparationM2100%IR 4.125
countryM4100%IR 497.4
upper_depth_cmM146100%0 – 210
lower_depth_cmM175100%1 – 254
texture_classM13675.78%IR 511.7
pedon_taxaM85092.08%IR 153
horizon_designationM25796.48%IR 851
oc_usda_c729_w_pctY1,151100%-0.028 – 53.88
c_tot_usda_a622_w_pctY2,01198.22%0.0161 – 53.88
n_tot_usda_a623_w_pctY1,919100%0 – 3.02
s_tot_usda_a624_w_pctY1,17798.22%0 – 18.38
ph_h2o_usda_a268_indexY49499.8%3.69 – 9.52
bd_usda_a4_g_cm3Y95747.91%0.3004 – 2.033
clay_tot_usda_a334_w_pctY2,083100%0 – 86.69
silt_tot_usda_c62_w_pctY733100%0 – 87.9
sand_tot_usda_c60_w_pctY849100%0.3 – 100
caco3_usda_a54_w_pctY69134.47%-0.5671 – 89.03
efferv_usda_a479_classY598.03%IR 14.06
cec_usda_a723_cmolc_kgY2,07999.8%0.134 – 190.1
ca_ext_usda_a722_cmolc_kgY1,96599.8%0 – 363.6
mg_ext_usda_a724_cmolc_kgY1,96699.8%0 – 82.14
k_ext_usda_a725_cmolc_kgY1,91199.8%0 – 11.25
na_ext_usda_a726_cmolc_kgY74599.8%0 – 203
wr_33kpa_usda_a415_w_pctY1998.769%1.003 – 127.9
wr_1500kpa_usda_a417_w_pctY2,00098.07%0.07522 – 96.14
al_dith_usda_a65_w_pctY1,60185.34%0 – 2.285
p_ext_usda_a652_mg_kgY71834.6%0 – 1359
p_ext_usda_a1070_mg_kgY763.338%0.3618 – 256.4
k_ext_usda_a1065_mg_kgY763.338%0 – 730.2
ec_usda_a364_ds_mY71246.56%0.012 – 81.93

Identifiers and sampling groups remain available separately. Statistics count exported observations.

Variables

Declared prediction targets and recorded metadata, with distributions and missing-value counts. Identifiers are not automatically prediction targets. Reading guide

Targets 23

oc_usda_c729_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

oc_usda_c729_w_pct distribution02,5005,0007,500-0.028 – 2.218: 72842.218 – 4.464: 18664.464 – 6.711: 6006.711 – 8.957: 2358.957 – 11.2: 7211.2 – 13.45: 2813.45 – 15.7: 015.7 – 17.94: 717.94 – 20.19: 020.19 – 22.43: 722.43 – 24.68: 024.68 – 26.93: 026.93 – 29.17: 029.17 – 31.42: 031.42 – 33.66: 033.66 – 35.91: 035.91 – 38.16: 738.16 – 40.4: 740.4 – 42.65: 042.65 – 44.9: 044.9 – 47.14: 047.14 – 49.39: 749.39 – 51.63: 051.63 – 53.88: 7-200204060
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.946 ± 3.02
Median
?50th percentile: half the non-missing values lie at or below it.
1.21
Range
?Smallest and largest recorded non-missing values.
-0.028 – 53.88
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.55
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
9.1 / 1.3e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

c_tot_usda_a622_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

c_tot_usda_a622_w_pct distribution02,5005,0007,5000.0161 – 2.26: 65762.26 – 4.505: 22034.505 – 6.749: 7346.749 – 8.993: 2898.993 – 11.24: 7211.24 – 13.48: 3113.48 – 15.73: 015.73 – 17.97: 717.97 – 20.21: 020.21 – 22.46: 722.46 – 24.7: 024.7 – 26.95: 026.95 – 29.19: 029.19 – 31.44: 031.44 – 33.68: 033.68 – 35.93: 035.93 – 38.17: 738.17 – 40.41: 740.41 – 42.66: 042.66 – 44.9: 044.9 – 47.15: 047.15 – 49.39: 749.39 – 51.64: 051.64 – 53.88: 70204060
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 180
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2.233 ± 3.08
Median
?50th percentile: half the non-missing values lie at or below it.
1.5
Range
?Smallest and largest recorded non-missing values.
0.0161 – 53.88
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.38
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
8.6 / 1.2e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

n_tot_usda_a623_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

n_tot_usda_a623_w_pct distribution02,0004,0006,0000 – 0.1258: 53910.1258 – 0.2516: 28770.2516 – 0.3775: 11400.3775 – 0.5033: 3790.5033 – 0.6291: 1920.6291 – 0.7549: 880.7549 – 0.8808: 120.8808 – 1.007: 71.007 – 1.132: 01.132 – 1.258: 61.258 – 1.384: 01.384 – 1.51: 01.51 – 1.636: 71.636 – 1.762: 01.762 – 1.887: 01.887 – 2.013: 02.013 – 2.139: 02.139 – 2.265: 02.265 – 2.391: 02.391 – 2.516: 02.516 – 2.642: 142.642 – 2.768: 02.768 – 2.894: 72.894 – 3.02: 701234
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.1614 ± 0.196
Median
?50th percentile: half the non-missing values lie at or below it.
0.1165
Range
?Smallest and largest recorded non-missing values.
0 – 3.02
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.22
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
7.2 / 87
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

s_tot_usda_a624_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

s_tot_usda_a624_w_pct distribution05,00010,0000 – 0.7659: 96810.7659 – 1.532: 981.532 – 2.298: 382.298 – 3.063: 683.063 – 3.829: 93.829 – 4.595: 04.595 – 5.361: 05.361 – 6.127: 36.127 – 6.893: 06.893 – 7.659: 107.659 – 8.424: 08.424 – 9.19: 69.19 – 9.956: 09.956 – 10.72: 010.72 – 11.49: 611.49 – 12.25: 012.25 – 13.02: 613.02 – 13.79: 013.79 – 14.55: 014.55 – 15.32: 615.32 – 16.08: 016.08 – 16.85: 316.85 – 17.61: 017.61 – 18.38: 1305101520
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 180
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.1315 ± 1
Median
?50th percentile: half the non-missing values lie at or below it.
0.01332
Range
?Smallest and largest recorded non-missing values.
0 – 18.38
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
7.63
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
14 / 2.1e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

ph_h2o_usda_a268_index

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

ph_h2o_usda_a268_index distribution02505007503.69 – 3.933: 163.933 – 4.176: 1524.176 – 4.419: 3334.419 – 4.662: 5324.662 – 4.905: 5334.905 – 5.147: 6615.147 – 5.39: 7175.39 – 5.633: 6915.633 – 5.876: 6605.876 – 6.119: 6396.119 – 6.362: 5656.362 – 6.605: 5296.605 – 6.848: 5236.848 – 7.091: 4727.091 – 7.334: 3887.334 – 7.577: 3477.577 – 7.82: 5527.82 – 8.062: 7508.062 – 8.305: 5078.305 – 8.548: 3198.548 – 8.791: 1438.791 – 9.034: 559.034 – 9.277: 79.277 – 9.52: 1612510
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
6.307 ± 1.27
Median
?50th percentile: half the non-missing values lie at or below it.
6.17
Range
?Smallest and largest recorded non-missing values.
3.69 – 9.52
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.202
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.16 / -1.1
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

bd_usda_a4_g_cm3

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

bd_usda_a4_g_cm3 distribution02505007500.3004 – 0.3726: 70.3726 – 0.4448: 100.4448 – 0.517: 00.517 – 0.5892: 280.5892 – 0.6614: 120.6614 – 0.7336: 280.7336 – 0.8058: 510.8058 – 0.878: 990.878 – 0.9502: 1090.9502 – 1.022: 1791.022 – 1.095: 2311.095 – 1.167: 3231.167 – 1.239: 4331.239 – 1.311: 6171.311 – 1.383: 6281.383 – 1.456: 6561.456 – 1.528: 6021.528 – 1.6: 3521.6 – 1.672: 2491.672 – 1.744: 1141.744 – 1.817: 831.817 – 1.889: 171.889 – 1.961: 171.961 – 2.033: 70.00.51.01.52.02.5
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 5,275
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.323 ± 0.24
Median
?50th percentile: half the non-missing values lie at or below it.
1.346
Range
?Smallest and largest recorded non-missing values.
0.3004 – 2.033
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.181
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.61 / 1
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

clay_tot_usda_a334_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

clay_tot_usda_a334_w_pct distribution05001,0001,5000 – 3.612: 10833.612 – 7.224: 10377.224 – 10.84: 92810.84 – 14.45: 102014.45 – 18.06: 91818.06 – 21.67: 95121.67 – 25.28: 99025.28 – 28.9: 75628.9 – 32.51: 49832.51 – 36.12: 49036.12 – 39.73: 45339.73 – 43.34: 30343.34 – 46.96: 18046.96 – 50.57: 17550.57 – 54.18: 11854.18 – 57.79: 5757.79 – 61.4: 3561.4 – 65.02: 3765.02 – 68.63: 4168.63 – 72.24: 972.24 – 75.85: 2575.85 – 79.47: 1179.47 – 83.08: 483.08 – 86.69: 80255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
20.3 ± 14.4
Median
?50th percentile: half the non-missing values lie at or below it.
18.22
Range
?Smallest and largest recorded non-missing values.
0 – 86.69
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.709
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.92 / 0.95
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

silt_tot_usda_c62_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

silt_tot_usda_c62_w_pct distribution05001,0000 – 3.663: 2393.663 – 7.325: 3757.325 – 10.99: 46710.99 – 14.65: 51614.65 – 18.31: 46018.31 – 21.98: 38421.98 – 25.64: 56825.64 – 29.3: 51229.3 – 32.96: 62232.96 – 36.62: 61936.62 – 40.29: 81740.29 – 43.95: 59143.95 – 47.61: 55447.61 – 51.27: 58051.27 – 54.94: 52854.94 – 58.6: 37158.6 – 62.26: 46262.26 – 65.92: 36465.92 – 69.59: 26669.59 – 73.25: 26573.25 – 76.91: 22776.91 – 80.58: 11880.58 – 84.24: 15484.24 – 87.9: 680255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
38.45 ± 20.7
Median
?50th percentile: half the non-missing values lie at or below it.
38.1
Range
?Smallest and largest recorded non-missing values.
0 – 87.9
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.537
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.17 / -0.76
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

sand_tot_usda_c60_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

sand_tot_usda_c60_w_pct distribution05001,0000.3 – 4.454: 9974.454 – 8.608: 6418.608 – 12.76: 49812.76 – 16.92: 48116.92 – 21.07: 53221.07 – 25.23: 47725.23 – 29.38: 47129.38 – 33.53: 38933.53 – 37.69: 47437.69 – 41.84: 50941.84 – 46: 41946 – 50.15: 41550.15 – 54.3: 41354.3 – 58.46: 37958.46 – 62.61: 31462.61 – 66.77: 49166.77 – 70.92: 32870.92 – 75.08: 23975.08 – 79.23: 26279.23 – 83.38: 28783.38 – 87.54: 38387.54 – 91.69: 32491.69 – 95.85: 23495.85 – 100: 1700255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
41.24 ± 28.4
Median
?50th percentile: half the non-missing values lie at or below it.
38.2
Range
?Smallest and largest recorded non-missing values.
0.3 – 100
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.689
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.3 / -1.1
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

caco3_usda_a54_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

caco3_usda_a54_w_pct distribution01,0002,0003,000-0.5671 – 3.166: 20303.166 – 6.899: 4186.899 – 10.63: 28110.63 – 14.37: 19714.37 – 18.1: 15518.1 – 21.83: 10921.83 – 25.57: 11125.57 – 29.3: 7529.3 – 33.03: 6233.03 – 36.77: 1336.77 – 40.5: 1040.5 – 44.23: 044.23 – 47.97: 047.97 – 51.7: 1051.7 – 55.43: 1055.43 – 59.16: 059.16 – 62.9: 062.9 – 66.63: 066.63 – 70.36: 770.36 – 74.1: 074.1 – 77.83: 077.83 – 81.56: 081.56 – 85.3: 085.3 – 89.03: 3-250255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 6,636
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
6.306 ± 9.63
Median
?50th percentile: half the non-missing values lie at or below it.
1.756
Range
?Smallest and largest recorded non-missing values.
-0.5671 – 89.03
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.53
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.6 / 10
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

efferv_usda_a479_class

target · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

efferv_usda_a479_class classesnonenone: 7,1137,113very slightvery slight: 1,0531,053violentviolent: 654654slightslight: 601601strongstrong: 506506
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 200
Classes
?Number of distinct recorded category values.
5
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.61
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1e+01
Top class
?Most frequent recorded category and its observation count.
none (7,113)

cec_usda_a723_cmolc_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

cec_usda_a723_cmolc_kg distribution02,0004,0000.134 – 8.05: 26538.05 – 15.97: 317515.97 – 23.88: 227823.88 – 31.8: 121931.8 – 39.71: 48139.71 – 47.63: 13647.63 – 55.54: 8655.54 – 63.46: 1863.46 – 71.38: 1271.38 – 79.29: 1479.29 – 87.21: 087.21 – 95.12: 095.12 – 103: 7103 – 111: 0111 – 118.9: 7118.9 – 126.8: 0126.8 – 134.7: 0134.7 – 142.6: 0142.6 – 150.5: 0150.5 – 158.5: 14158.5 – 166.4: 0166.4 – 174.3: 0174.3 – 182.2: 0182.2 – 190.1: 7050100150200
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
16.14 ± 12.9
Median
?50th percentile: half the non-missing values lie at or below it.
14.08
Range
?Smallest and largest recorded non-missing values.
0.134 – 190.1
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.801
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
4.5 / 45
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

ca_ext_usda_a722_cmolc_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

ca_ext_usda_a722_cmolc_kg distribution02,5005,0007,5000 – 15.15: 647215.15 – 30.3: 161630.3 – 45.46: 74445.46 – 60.61: 72160.61 – 75.76: 20575.76 – 90.91: 9390.91 – 106.1: 48106.1 – 121.2: 53121.2 – 136.4: 6136.4 – 151.5: 16151.5 – 166.7: 16166.7 – 181.8: 20181.8 – 197: 7197 – 212.1: 7212.1 – 227.3: 9227.3 – 242.4: 9242.4 – 257.6: 10257.6 – 272.7: 10272.7 – 287.9: 10287.9 – 303: 16303 – 318.2: 0318.2 – 333.3: 6333.3 – 348.5: 6348.5 – 363.6: 70100200300400
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
20.05 ± 33.5
Median
?50th percentile: half the non-missing values lie at or below it.
9.953
Range
?Smallest and largest recorded non-missing values.
0 – 363.6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.67
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
5 / 35
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

mg_ext_usda_a724_cmolc_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

mg_ext_usda_a724_cmolc_kg distribution02,5005,0007,5000 – 3.422: 67483.422 – 6.845: 20216.845 – 10.27: 74010.27 – 13.69: 25513.69 – 17.11: 13917.11 – 20.53: 8820.53 – 23.96: 5123.96 – 27.38: 2527.38 – 30.8: 030.8 – 34.22: 034.22 – 37.65: 1537.65 – 41.07: 741.07 – 44.49: 044.49 – 47.91: 647.91 – 51.34: 051.34 – 54.76: 054.76 – 58.18: 658.18 – 61.6: 061.6 – 65.02: 065.02 – 68.45: 068.45 – 71.87: 071.87 – 75.29: 075.29 – 78.71: 078.71 – 82.14: 60255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
3.44 ± 4.88
Median
?50th percentile: half the non-missing values lie at or below it.
2.02
Range
?Smallest and largest recorded non-missing values.
0 – 82.14
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.42
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
5.4 / 58
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

k_ext_usda_a725_cmolc_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

k_ext_usda_a725_cmolc_kg distribution02,5005,0007,5000 – 0.4687: 62970.4687 – 0.9373: 22860.9373 – 1.406: 7711.406 – 1.875: 3441.875 – 2.343: 1762.343 – 2.812: 582.812 – 3.281: 813.281 – 3.749: 253.749 – 4.218: 144.218 – 4.687: 164.687 – 5.155: 95.155 – 5.624: 125.624 – 6.093: 06.093 – 6.561: 96.561 – 7.03: 07.03 – 7.499: 07.499 – 7.967: 07.967 – 8.436: 38.436 – 8.905: 38.905 – 9.373: 09.373 – 9.842: 09.842 – 10.31: 010.31 – 10.78: 010.78 – 11.25: 3051015
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.5371 ± 0.694
Median
?50th percentile: half the non-missing values lie at or below it.
0.3343
Range
?Smallest and largest recorded non-missing values.
0 – 11.25
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.29
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
4.5 / 37
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

na_ext_usda_a726_cmolc_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

na_ext_usda_a726_cmolc_kg distribution05,00010,0000 – 8.457: 98798.457 – 16.91: 9216.91 – 25.37: 3725.37 – 33.83: 2733.83 – 42.28: 742.28 – 50.74: 050.74 – 59.2: 1359.2 – 67.65: 067.65 – 76.11: 076.11 – 84.57: 084.57 – 93.03: 093.03 – 101.5: 0101.5 – 109.9: 6109.9 – 118.4: 0118.4 – 126.9: 7126.9 – 135.3: 27135.3 – 143.8: 0143.8 – 152.2: 6152.2 – 160.7: 0160.7 – 169.1: 0169.1 – 177.6: 0177.6 – 186.1: 0186.1 – 194.5: 0194.5 – 203: 60100200300
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 20
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.27 ± 10.4
Median
?50th percentile: half the non-missing values lie at or below it.
0
Range
?Smallest and largest recorded non-missing values.
0 – 203
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
8.15
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
13 / 1.9e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

wr_33kPa_usda_a415_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

wr_33kPa_usda_a415_w_pct distribution01002001.003 – 6.291: 766.291 – 11.58: 10011.58 – 16.87: 10716.87 – 22.15: 12422.15 – 27.44: 17727.44 – 32.73: 13032.73 – 38.02: 10038.02 – 43.3: 3643.3 – 48.59: 2148.59 – 53.88: 753.88 – 59.16: 359.16 – 64.45: 064.45 – 69.74: 069.74 – 75.03: 075.03 – 80.31: 080.31 – 85.6: 085.6 – 90.89: 090.89 – 96.18: 096.18 – 101.5: 0101.5 – 106.8: 0106.8 – 112: 0112 – 117.3: 0117.3 – 122.6: 0122.6 – 127.9: 7050100150
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 9,239
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
23.6 ± 14.8
Median
?50th percentile: half the non-missing values lie at or below it.
23.9
Range
?Smallest and largest recorded non-missing values.
1.003 – 127.9
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.627
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.7 / 18
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

wr_1500kPa_usda_a417_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

wr_1500kPa_usda_a417_w_pct distribution01,0002,0003,0000.07522 – 4.078: 13544.078 – 8.081: 20408.081 – 12.08: 244612.08 – 16.09: 202916.09 – 20.09: 104320.09 – 24.09: 52524.09 – 28.09: 26428.09 – 32.1: 13232.1 – 36.1: 3536.1 – 40.1: 2940.1 – 44.1: 044.1 – 48.11: 048.11 – 52.11: 752.11 – 56.11: 056.11 – 60.12: 060.12 – 64.12: 064.12 – 68.12: 068.12 – 72.12: 772.12 – 76.13: 1476.13 – 80.13: 080.13 – 84.13: 084.13 – 88.13: 088.13 – 92.14: 092.14 – 96.14: 70255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 195
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
11.57 ± 7.57
Median
?50th percentile: half the non-missing values lie at or below it.
10.71
Range
?Smallest and largest recorded non-missing values.
0.07522 – 96.14
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.654
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.7 / 20
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

al_dith_usda_a65_w_pct

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

al_dith_usda_a65_w_pct distribution02,0004,0000 – 0.09519: 39060.09519 – 0.1904: 25460.1904 – 0.2856: 9110.2856 – 0.3808: 2800.3808 – 0.476: 2500.476 – 0.5712: 1700.5712 – 0.6664: 1010.6664 – 0.7616: 1070.7616 – 0.8567: 1150.8567 – 0.9519: 270.9519 – 1.047: 351.047 – 1.142: 351.142 – 1.238: 291.238 – 1.333: 451.333 – 1.428: 101.428 – 1.523: 221.523 – 1.618: 211.618 – 1.713: 131.713 – 1.809: 51.809 – 1.904: 01.904 – 1.999: 01.999 – 2.094: 02.094 – 2.189: 02.189 – 2.285: 140123
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 1,485
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.1799 ± 0.258
Median
?50th percentile: half the non-missing values lie at or below it.
0.1082
Range
?Smallest and largest recorded non-missing values.
0 – 2.285
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.44
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
3.5 / 16
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

p_ext_usda_a652_mg_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

p_ext_usda_a652_mg_kg distribution02,0004,0000 – 56.62: 305956.62 – 113.2: 281113.2 – 169.9: 62169.9 – 226.5: 41226.5 – 283.1: 12283.1 – 339.7: 22339.7 – 396.3: 6396.3 – 453: 3453 – 509.6: 6509.6 – 566.2: 6566.2 – 622.8: 3622.8 – 679.5: 0679.5 – 736.1: 0736.1 – 792.7: 0792.7 – 849.3: 0849.3 – 905.9: 0905.9 – 962.6: 0962.6 – 1019: 01019 – 1076: 01076 – 1132: 01132 – 1189: 01189 – 1246: 01246 – 1302: 01302 – 1359: 305001,0001,500
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 6,623
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
30.27 ± 68.9
Median
?50th percentile: half the non-missing values lie at or below it.
10.29
Range
?Smallest and largest recorded non-missing values.
0 – 1359
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
2.27
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
8.9 / 1.3e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

p_ext_usda_a1070_mg_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

p_ext_usda_a1070_mg_kg distribution0501001500.3618 – 11.03: 13611.03 – 21.7: 7321.7 – 32.37: 3432.37 – 43.04: 3343.04 – 53.71: 1053.71 – 64.38: 2264.38 – 75.05: 1075.05 – 85.72: 785.72 – 96.39: 1096.39 – 107.1: 0107.1 – 117.7: 0117.7 – 128.4: 0128.4 – 139.1: 0139.1 – 149.7: 0149.7 – 160.4: 0160.4 – 171.1: 0171.1 – 181.8: 0181.8 – 192.4: 0192.4 – 203.1: 0203.1 – 213.8: 0213.8 – 224.4: 0224.4 – 235.1: 0235.1 – 245.8: 0245.8 – 256.4: 30100200300
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 9,789
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
26.28 ± 32.2
Median
?50th percentile: half the non-missing values lie at or below it.
15.91
Range
?Smallest and largest recorded non-missing values.
0.3618 – 256.4
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.23
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
3.6 / 21
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

k_ext_usda_a1065_mg_kg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

k_ext_usda_a1065_mg_kg distribution02550750 – 30.42: 1230.42 – 60.85: 2960.85 – 91.27: 6191.27 – 121.7: 30121.7 – 152.1: 61152.1 – 182.5: 23182.5 – 213: 26213 – 243.4: 18243.4 – 273.8: 29273.8 – 304.2: 9304.2 – 334.7: 9334.7 – 365.1: 6365.1 – 395.5: 10395.5 – 425.9: 3425.9 – 456.4: 3456.4 – 486.8: 3486.8 – 517.2: 0517.2 – 547.6: 0547.6 – 578.1: 0578.1 – 608.5: 3608.5 – 638.9: 0638.9 – 669.3: 0669.3 – 699.8: 0699.8 – 730.2: 30200400600800
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 9,789
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
171.2 ± 122
Median
?50th percentile: half the non-missing values lie at or below it.
136.2
Range
?Smallest and largest recorded non-missing values.
0 – 730.2
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.712
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.6 / 3.9
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

ec_usda_a364_ds_m

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

ec_usda_a364_ds_m distribution02,0004,0006,0000.012 – 3.425: 44843.425 – 6.839: 1016.839 – 10.25: 3310.25 – 13.67: 1213.67 – 17.08: 1317.08 – 20.49: 020.49 – 23.91: 023.91 – 27.32: 2027.32 – 30.73: 030.73 – 34.15: 034.15 – 37.56: 037.56 – 40.97: 040.97 – 44.39: 3244.39 – 47.8: 047.8 – 51.21: 051.21 – 54.63: 054.63 – 58.04: 058.04 – 61.45: 761.45 – 64.87: 664.87 – 68.28: 068.28 – 71.69: 071.69 – 75.11: 075.11 – 78.52: 078.52 – 81.93: 70255075100
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 5,412
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.341 ± 5.99
Median
?50th percentile: half the non-missing values lie at or below it.
0.231
Range
?Smallest and largest recorded non-missing values.
0.012 – 81.93
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
4.47
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
8.8 / 87
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Metadata 19

ID_sample

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

ID_sample classes6792267922: 2020115599115599: 2020120566120566: 2020120929120929: 2020121256121256: 2020122183122183: 2020122207122207: 2020130405130405: 99131802131802: 99146181146181: 99+10 more+10 more: 8484
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2,106
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.99
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
10
Top class
?Most frequent recorded category and its observation count.
67922 (20)

SpectralRep

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

SpectralRep distribution02,0004,0006,0001 – 1.75: 40821.75 – 2.5: 21052.5 – 3.25: 11153.25 – 4: 04 – 4.75: 11124.75 – 5.5: 10835.5 – 6.25: 5196.25 – 7: 07 – 7.75: 177.75 – 8.5: 178.5 – 9.25: 79.25 – 10: 010 – 10.75: 710.75 – 11.5: 711.5 – 12.25: 712.25 – 13: 013 – 13.75: 713.75 – 14.5: 714.5 – 15.25: 715.25 – 16: 016 – 16.75: 716.75 – 17.5: 717.5 – 18.25: 718.25 – 19: 705101520
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2.562 ± 1.92
Median
?50th percentile: half the non-missing values lie at or below it.
2
Range
?Smallest and largest recorded non-missing values.
1 – 19
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.75
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.4 / 12
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

collection_name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

collection_name classesneospectra_nirneospectra_nir: 8,1518,151neospectra_mirneospectra_mir: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
neospectra_nir (8,151)

dataset_code

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

dataset_code classesNEOSPECTRA.NIRNEOSPECTRA.NIR: 8,1518,151NEOSPECTRA.MIRNEOSPECTRA.MIR: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
NEOSPECTRA.NIR (8,151)

spectral_kind

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

spectral_kind classesnirnir: 8,1518,151mirmir: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
nir (8,151)

scan_local_id

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

scan_local_id classesNEO2_067922NEO2_067922: 44NEO2_115599NEO2_115599: 44NEO2_120566NEO2_120566: 44NEO2_120929NEO2_120929: 44NEO2_121256NEO2_121256: 44NEO2_122183NEO2_122183: 44NEO2_122207NEO2_122207: 44NEO3_067922NEO3_067922: 44NEO3_115599NEO3_115599: 44NEO3_120566NEO3_120566: 44+10 more+10 more: 4040
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 1,976
Classes
?Number of distinct recorded category values.
8,067
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
NEO2_067922 (4)

scan_lab

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

scan_lab classesKSSLKSSL: 5,0095,009WoodwellWoodwell: 3,1423,142
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 1,976
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.96
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2
Top class
?Most frequent recorded category and its observation count.
KSSL (5,009)

scan_model_name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

scan_model_name classesBruker Vertex 70 with HTS-XT …Bruker Vertex 70 with HTS-XT accessory: 1,9761,976NEO3NEO3: 1,1321,132NEO2NEO2: 1,1311,131NEO6NEO6: 1,1301,130NEO5NEO5: 1,1281,128NEO8NEO8: 996996NEO9NEO9: 996996NEO7NEO7: 600600NEO1NEO1: 519519NEO4NEO4: 519519
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
10
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.97
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
Bruker Vertex 70 with HTS-XT accessory (1,976)

scan_model_code

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

scan_model_code classes2102007621020076: 1,1321,1322102007521020075: 1,1311,1312102008121020081: 1,1301,1302102007221020072: 1,1281,1282203007522030075: 9969962203007622030076: 9969962106003521060035: 6006002102003321020033: 5195192102007321020073: 519519
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 1,976
Classes
?Number of distinct recorded category values.
9
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.98
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2
Top class
?Most frequent recorded category and its observation count.
21020076 (1,132)

scan_preparation

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

scan_preparation classes<2 mm<2 mm: 8,1518,151Finely ground <80 meshFinely ground <80 mesh: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
<2 mm (8,151)

country_iso3166

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

country_iso3166 classesUSAUSA: 9,9479,947GHAGHA: 132132NGANGA: 2828KENKEN: 2020
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
4
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.074
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
5e+02
Top class
?Most frequent recorded category and its observation count.
USA (9,947)

upper_depth_cm

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

upper_depth_cm distribution02,0004,0006,0000 – 8.75: 52868.75 – 17.5: 185017.5 – 26.25: 77726.25 – 35: 30935 – 43.75: 22143.75 – 52.5: 22152.5 – 61.25: 18161.25 – 70: 16870 – 78.75: 19878.75 – 87.5: 14987.5 – 96.25: 15796.25 – 105: 97105 – 113.8: 109113.8 – 122.5: 78122.5 – 131.2: 83131.2 – 140: 51140 – 148.8: 35148.8 – 157.5: 52157.5 – 166.2: 45166.2 – 175: 16175 – 183.8: 14183.8 – 192.5: 22192.5 – 201.2: 6201.2 – 210: 20100200300
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
21.6 ± 35.2
Median
?50th percentile: half the non-missing values lie at or below it.
8
Range
?Smallest and largest recorded non-missing values.
0 – 210
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.63
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.3 / 5.1
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

lower_depth_cm

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

lower_depth_cm distribution02,0004,0001 – 11.54: 204811.54 – 22.08: 319222.08 – 32.62: 220532.62 – 43.17: 30243.17 – 53.71: 23853.71 – 64.25: 37264.25 – 74.79: 23974.79 – 85.33: 16485.33 – 95.88: 14895.88 – 106.4: 226106.4 – 117: 133117 – 127.5: 157127.5 – 138: 157138 – 148.6: 51148.6 – 159.1: 89159.1 – 169.7: 76169.7 – 180.2: 105180.2 – 190.8: 37190.8 – 201.3: 93201.3 – 211.8: 52211.8 – 222.4: 13222.4 – 232.9: 7232.9 – 243.5: 9243.5 – 254: 140100200300
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
38.71 ± 44.4
Median
?50th percentile: half the non-missing values lie at or below it.
21
Range
?Smallest and largest recorded non-missing values.
1 – 254
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.15
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.2 / 4.3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

texture_class

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

texture_class classesSilt loamSilt loam: 1,5351,535LoamLoam: 844844Silty clay loamSilty clay loam: 750750Clay loamClay loam: 409409Fine sandy loamFine sandy loam: 386386Sandy loamSandy loam: 349349SandSand: 324324Silty claySilty clay: 239239Loamy sandLoamy sand: 221221Sandy clay loamSandy clay loam: 212212+10 more+10 more: 1,1361,136
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 2,453
Classes
?Number of distinct recorded category values.
136
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.68
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
5e+02
Top class
?Most frequent recorded category and its observation count.
Silt loam (1,535)

pedon_taxa

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

pedon_taxa classesMixed, thermic Lamellic Ustip…Mixed, thermic Lamellic Ustipsamment: 306306Fine-silty, mixed, active, me…Fine-silty, mixed, active, mesic Oxyaquic Fragiudalf: 139139Fine-loamy, mixed, superactiv…Fine-loamy, mixed, superactive, frigid Typic Argiustoll: 129129Fine, smectitic, mesic Oxyaqu…Fine, smectitic, mesic Oxyaquic Vertic Argiudoll: 115115Sandy, mixed, frigid Entic Ha…Sandy, mixed, frigid Entic Haplorthod: 112112Mixed, mesic Typic UdipsammentMixed, mesic Typic Udipsamment: 108108Fine, smectitic, thermic Oxya…Fine, smectitic, thermic Oxyaquic Vertic Argiudoll: 9797Fine, kaolinitic, thermic Typ…Fine, kaolinitic, thermic Typic Kanhapludult: 8888Fine, illitic, mesic Aeric Ep…Fine, illitic, mesic Aeric Epiaqualf: 7878Sandy, mixed, mesic Typic End…Sandy, mixed, mesic Typic Endoaquod: 7676+10 more+10 more: 526526
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 802
Classes
?Number of distinct recorded category values.
850
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.93
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
153
Top class
?Most frequent recorded category and its observation count.
Mixed, thermic Lamellic Ustipsamment (306)

horizon_designation

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

horizon_designation classesAA: 1,7021,702ApAp: 1,2031,203A1A1: 453453Ap1Ap1: 429429Bt1Bt1: 422422EE: 419419A2A2: 384384Ap2Ap2: 298298Bw1Bw1: 253253Bs1Bs1: 227227+10 more+10 more: 1,4001,400
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 356
Classes
?Number of distinct recorded category values.
257
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.69
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
851
Top class
?Most frequent recorded category and its observation count.
A (1,702)

feature_count_per_dimension

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

feature_count_per_dimension distribution05,00010,000601 – 646.8: 8151646.8 – 692.7: 0692.7 – 738.5: 0738.5 – 784.3: 0784.3 – 830.2: 0830.2 – 876: 0876 – 921.8: 0921.8 – 967.7: 0967.7 – 1014: 01014 – 1059: 01059 – 1105: 01105 – 1,151: 01,151 – 1197: 01197 – 1243: 01243 – 1288: 01288 – 1334: 01334 – 1380: 01380 – 1,426: 01,426 – 1472: 01472 – 1518: 01518 – 1564: 01564 – 1609: 01609 – 1655: 01655 – 1,701: 19761002005001,0002,0005,00010,000
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
815.6 ± 436
Median
?50th percentile: half the non-missing values lie at or below it.
601
Range
?Smallest and largest recorded non-missing values.
601 – 1,701
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.534
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.5 / 0.37
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

wavelength_note

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

wavelength_note classesneospectra_nir_1350_2550_nm_s…neospectra_nir_1350_2550_nm_step_2: 8,1518,151neospectra_mir_600_4000_cm_mi…neospectra_mir_600_4000_cm_minus_1_step_2: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
neospectra_nir_1350_2550_nm_step_2 (8,151)

replaces_dataset_id

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

replaces_dataset_id classesossl_regression_b031e1af | os…ossl_regression_b031e1af | ossl_soil_ph_h2o_usda_a268_index_neospectra_nirs_ossl: 8,1518,151ossl_regression_09efb2d8ossl_regression_09efb2d8: 1,9761,976
n / missing
?Recorded observation count and number of missing values for this variable.
10,127 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
ossl_regression_b031e1af | ossl_soil_ph_h2o_usda_a268_index_neospectra_nirs_ossl (8,151)
Constant metadata 5

Fields with one recorded value provide context for every observation; they have no distribution to compare.

  • datasetNeospectra database v1.2
  • dataset_ownerUSDA-NRCS-NSSC-KSSL, WCRC, UNL, iSDA
  • scan_opticsKSSL SOPs
  • dimensions1D
  • active_statusactive

6 variable(s) omitted (no recorded values).

Alignment

How observations join to physical sample IDs. Multiple exported rows are not necessarily independent samples or technical repetitions. Reading guide

Alignment levelsample
Sample id availableyes
Samples2,106
Observations (total)10,127
Exported observations per unitmin 1 · mean 2.481 · max 19
Identity statusavailable
Documented technical repetitionsND
Source unitMultiple spectra exported per identifier; nature not established

Splits

Partitions supplied by the original dataset. They are documented here and are not applied automatically when loading. Reading guide

originalall: 2,106 documented · not applied

Load this dataset

Use the appropriate route below: an assembled dataset, original raw resources, or the documented access procedure. Reading guide

# Download original raw resources; no Dataverse token required
from nirs4all_datasets import retrieve
result = retrieve("ossl_neospectra_soil_all_y", prepare=False)
print(result["resources"])
# Raw resources may need dataset-specific assembly before analysis

Metadata downloads are available for public datasets only. The dataset bytes are never served here — fetch them from the origin / DOI above.

Read the documentation

Access and rights are separate

Start with provenance and governance at the top of a card. Provenance links to the original resources and publications. Governance records the license, redistribution conditions and content identity.

Open source access means an open original-source route is recorded. Restricted redistribution is the display label for the catalog's private tier: rights are restricted or not cleared for redistribution. It does not mean that the original source is necessarily private.

Raw reproducible identifies a retrieval route. Assembly incomplete means raw resources can be available while dataset-specific conversion is missing. The loading example uses retrieve(id, prepare=False) for these raw downloads; it returns resource metadata rather than X/Y arrays. A complete original-source recipe uses get(id). Two Harvard datasets require explicit guestbook answers.

Token required means no open automatic route is recorded. A Dataverse token works only once the canonical data have a configured hosted DOI. Manual access requires the original provider's documented steps. Availability labels describe the catalog routes, not a fresh live check of every URL.

Why is PCA on a dataset card?

Spectra have many correlated wavelengths. Principal component analysis (PCA) compresses their variation into a few orthogonal directions, called principal components (PCs). It is an exploratory summary of X, calculated without using the target Y.

The score plot places each plotted observation on PC1 and PC2. Nearby dots have similar projections; groups can reveal batches, instruments or natural subpopulations. Axis percentages show explained spectral variance. Large sources are sampled to at most 800 observations. Similar positions in two dimensions do not guarantee similar full spectra; an isolated dot does not prove an acquisition fault.

The scree plot reports spectral variance per component, helping you see whether variation is concentrated in a few dimensions. It is not explained target variance or predictive accuracy. The scree projection and score-plot projection may use different row samples; their percentages need not match.

Q / SPE measures residual variation outside the retained PCA subspace. Hotelling T² measures an observation's scaled distance within that subspace; Mahalanobis H summarizes PCA-space distance. The displayed p95/median ratios compare the distribution's upper tail with its center, without an absolute rejection threshold.

Historical profiles replace missing cells with column medians before PCA, center channels, and use bounded row/component counts. Loadings and distances depend on signal units and preprocessing. Compare compatible sources and inspect the original spectra before acting.

Scores, evidence and missing values

Read measured values first. An alert score maps selected metrics to a heuristic 0–1 scale. The display bands are weak below 0.42, medium from 0.42 to below 0.72, and strong from 0.72. They are not universally validated pass/fail thresholds.

The radar's center is 0 and its outer ring is 1: outward means a stronger anomaly or heterogeneity signal for that axis. Its table gives values rounded to two decimals and explains each axis. Missing axes are left blank; partial profiles have no filled polygon. The mean profile score averages only available axes and is not an overall quality grade.

Diagnostic bars rank possible explanations from heuristic signatures. A score of 0.78 does not mean a 78% causal probability. Evidence coverage is the proportion of required signals available, not confidence in the cause. A dash or “unavailable” means the quantity could not be computed; zero is an actual recorded value.

RMS (root mean square distance) and SAM (Spectral Angle Mapper, in radians) use this dataset's mean spectrum as their reference. They do not compare with an independently acquired white or dark reference. Repeated IDs require a documented protocol before within-ID spread can be called technical repeatability.

X–Y plots show Pearson correlation within this dataset. They identify linear associations; they are not cross-validated predictions and do not establish causal relationships. Targets, metadata, observation counts and physical sample counts have different meanings.

Open the full reading guide