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EcoSIS CABO 2018-2019 Leaf-Level Spectra (absorbance)

ecosis · NIR

EcoSIS CABO 2018-2019 Leaf-Level Spectra (absorbance). v2.0 standardized NIRS package: 1 spectral source(s), 32 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
1,971
samples
2,001
wavelengths
1
sources
32
targets
24
metadata
NIR
family

Dataset property explorer

Mean profile risk0.52
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS CABO 2018-2019 Leaf-Level Spectra (absorbance) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS CABO 2018-2019 Leaf-Level Spectra (absorbance) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.28PCA outliers: 1.00reference: 0.87repeatability: 0.00structure: 1.00EcoSIS CABO 201…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA1.00
Distance à la référence0.87
Répétabilité0.00
Baseline / forme0.28
Structure multi-régimes1.00
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.830.83Spectre hors domaine valideSpectre hors domaine valide: 0.710.71Signature VERA25-likeSignature VERA25-like: 0.680.68Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.650.65Dataset multi-régimesDataset multi-régimes: 0.620.62Erreur calibration / référenc…Erreur calibration / référence blanche: 0.540.54Différence de sonde / géométr…Différence de sonde / géométrie: 0.540.54Fond différentFond différent: 0.500.50
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.83forteSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Spectre hors domaine valideX0.71moyenneMahalanobis / T2 1.00, Structure PCA 1.00, RMS/SAM référence 0.87Variété, espèce, lot ou condition différente mais physiquement plausible.
Signature VERA25-likeX0.68moyenneMahalanobis / T2 1.00, Spike rate 1.00, Jump rate 1.00Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur interpolation / rééchantillonnageX0.65moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Dataset multi-régimesX0.62moyenneStructure PCA 1.00, Mahalanobis / T2 1.00, RMS/SAM référence 0.87Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Erreur calibration / référence blancheX0.54moyenneMahalanobis / T2 1.00, artefacts locaux 1.00, RMS/SAM référence 0.87Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX0.54moyenneMahalanobis / T2 1.00, RMS/SAM référence 0.87, PCA Q 0.56Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX0.50moyenneMahalanobis / T2 1.00, RMS/SAM référence 0.87, PCA Q 0.56Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.

Spectral sources

abs_spec.csv

X · NIR · Spectra Vista Corporation HR-1024i
abs_spec.csv spectra0.000.250.500.751.0005001,0001,5002,0002,500q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 0.9559 (q25–q75 0.9505–0.9606)414nm — median 0.9522 (q25–q75 0.9459–0.9581)429nm — median 0.9473 (q25–q75 0.9372–0.9533)443nm — median 0.9434 (q25–q75 0.9315–0.9501)458nm — median 0.9379 (q25–q75 0.9249–0.9457)472nm — median 0.9361 (q25–q75 0.9231–0.9445)486nm — median 0.9337 (q25–q75 0.9206–0.9427)501nm — median 0.9234 (q25–q75 0.9072–0.9339)515nm — median 0.8835 (q25–q75 0.8564–0.9032)529nm — median 0.8079 (q25–q75 0.7682–0.8425)544nm — median 0.7712 (q25–q75 0.7254–0.8125)558nm — median 0.7671 (q25–q75 0.7198–0.8086)573nm — median 0.8111 (q25–q75 0.7679–0.8461)587nm — median 0.8444 (q25–q75 0.8067–0.8734)601nm — median 0.8551 (q25–q75 0.8199–0.8816)616nm — median 0.8755 (q25–q75 0.8449–0.898)630nm — median 0.8851 (q25–q75 0.8568–0.9046)645nm — median 0.9012 (q25–q75 0.8784–0.9172)659nm — median 0.9168 (q25–q75 0.8993–0.9288)673nm — median 0.9284 (q25–q75 0.9151–0.9375)688nm — median 0.9082 (q25–q75 0.8906–0.9209)702nm — median 0.7322 (q25–q75 0.6804–0.7739)717nm — median 0.4652 (q25–q75 0.4152–0.5155)731nm — median 0.2712 (q25–q75 0.2408–0.3073)745nm — median 0.1735 (q25–q75 0.1564–0.1991)760nm — median 0.14 (q25–q75 0.1271–0.1644)774nm — median 0.1329 (q25–q75 0.1206–0.1564)788nm — median 0.1301 (q25–q75 0.1179–0.1533)803nm — median 0.1277 (q25–q75 0.1156–0.1507)817nm — median 0.1255 (q25–q75 0.1134–0.1482)832nm — median 0.1231 (q25–q75 0.111–0.1458)846nm — median 0.1201 (q25–q75 0.1084–0.1433)860nm — median 0.1174 (q25–q75 0.106–0.1408)875nm — median 0.115 (q25–q75 0.1037–0.1381)889nm — median 0.1133 (q25–q75 0.1021–0.1364)904nm — median 0.1117 (q25–q75 0.1006–0.1348)918nm — median 0.1105 (q25–q75 0.09928–0.1335)932nm — median 0.1102 (q25–q75 0.09917–0.1337)947nm — median 0.112 (q25–q75 0.1013–0.1356)961nm — median 0.1168 (q25–q75 0.1057–0.1413)976nm — median 0.1168 (q25–q75 0.1055–0.1417)990nm — median 0.1144 (q25–q75 0.1031–0.1392)1,004nm — median 0.1125 (q25–q75 0.1013–0.1372)1,019nm — median 0.1073 (q25–q75 0.09638–0.1315)1,033nm — median 0.1019 (q25–q75 0.09108–0.1245)1,047nm — median 0.09829 (q25–q75 0.08768–0.1205)1,062nm — median 0.09503 (q25–q75 0.08483–0.1171)1,076nm — median 0.0933 (q25–q75 0.08309–0.115)1,091nm — median 0.09248 (q25–q75 0.0825–0.1141)1,105nm — median 0.09251 (q25–q75 0.08226–0.1142)1,119nm — median 0.09294 (q25–q75 0.08256–0.1147)1,134nm — median 0.09831 (q25–q75 0.08769–0.1219)1,148nm — median 0.1127 (q25–q75 0.1005–0.1401)1,163nm — median 0.1242 (q25–q75 0.1103–0.154)1,177nm — median 0.1267 (q25–q75 0.1121–0.1573)1,191nm — median 0.1277 (q25–q75 0.1129–0.1593)1,206nm — median 0.1267 (q25–q75 0.1116–0.1573)1,220nm — median 0.1226 (q25–q75 0.1082–0.1528)1,235nm — median 0.1176 (q25–q75 0.1038–0.1463)1,249nm — median 0.1145 (q25–q75 0.1011–0.1421)1,263nm — median 0.113 (q25–q75 0.09991–0.1406)1,278nm — median 0.1128 (q25–q75 0.09971–0.1401)1,292nm — median 0.1157 (q25–q75 0.102–0.144)1,306nm — median 0.122 (q25–q75 0.1072–0.1526)1,321nm — median 0.1357 (q25–q75 0.1185–0.1701)1,335nm — median 0.1556 (q25–q75 0.1359–0.1957)1,350nm — median 0.1787 (q25–q75 0.1562–0.2248)1,364nm — median 0.2015 (q25–q75 0.1762–0.2529)1,378nm — median 0.2457 (q25–q75 0.2142–0.3056)1,393nm — median 0.3591 (q25–q75 0.3146–0.4372)1,407nm — median 0.4897 (q25–q75 0.4386–0.5741)1,422nm — median 0.5655 (q25–q75 0.5108–0.6491)1,436nm — median 0.5938 (q25–q75 0.5383–0.6766)1,450nm — median 0.5971 (q25–q75 0.5416–0.6808)1,465nm — median 0.5845 (q25–q75 0.5284–0.6687)1,479nm — median 0.5596 (q25–q75 0.5031–0.6448)1,494nm — median 0.5219 (q25–q75 0.4668–0.6095)1,508nm — median 0.4845 (q25–q75 0.4312–0.5727)1,522nm — median 0.4495 (q25–q75 0.398–0.5351)1,537nm — median 0.4156 (q25–q75 0.3663–0.4979)1,551nm — median 0.3886 (q25–q75 0.341–0.4678)1,565nm — median 0.3653 (q25–q75 0.3198–0.4421)1,580nm — median 0.3447 (q25–q75 0.3008–0.4167)1,594nm — median 0.3277 (q25–q75 0.2859–0.3966)1,609nm — median 0.3129 (q25–q75 0.2725–0.3782)1,623nm — median 0.3012 (q25–q75 0.2621–0.3641)1,637nm — median 0.2921 (q25–q75 0.2552–0.3528)1,652nm — median 0.2867 (q25–q75 0.2512–0.3463)1,666nm — median 0.2857 (q25–q75 0.2503–0.3434)1,681nm — median 0.2886 (q25–q75 0.2524–0.3473)1,695nm — median 0.2946 (q25–q75 0.2577–0.3538)1,709nm — median 0.3037 (q25–q75 0.2663–0.3629)1,724nm — median 0.314 (q25–q75 0.2754–0.3738)1,738nm — median 0.324 (q25–q75 0.2844–0.3856)1,753nm — median 0.3366 (q25–q75 0.2952–0.4008)1,767nm — median 0.3475 (q25–q75 0.3052–0.4155)1,781nm — median 0.3558 (q25–q75 0.3127–0.4257)1,796nm — median 0.3578 (q25–q75 0.3146–0.4297)1,810nm — median 0.3555 (q25–q75 0.3128–0.4281)1,824nm — median 0.3534 (q25–q75 0.311–0.4265)1,839nm — median 0.3574 (q25–q75 0.3145–0.432)1,853nm — median 0.3817 (q25–q75 0.3368–0.4604)1,868nm — median 0.4562 (q25–q75 0.4069–0.5397)1,882nm — median 0.5867 (q25–q75 0.5291–0.6687)1,896nm — median 0.7563 (q25–q75 0.7102–0.8113)1,911nm — median 0.8526 (q25–q75 0.8141–0.8885)1,925nm — median 0.8877 (q25–q75 0.8539–0.9172)1,940nm — median 0.8909 (q25–q75 0.8588–0.9205)1,954nm — median 0.8774 (q25–q75 0.8419–0.9105)1,968nm — median 0.8547 (q25–q75 0.8145–0.8939)1,983nm — median 0.8254 (q25–q75 0.7794–0.8723)1,997nm — median 0.7986 (q25–q75 0.7486–0.8537)2,012nm — median 0.7677 (q25–q75 0.7142–0.8304)2,026nm — median 0.7408 (q25–q75 0.6846–0.8089)2,040nm — median 0.7162 (q25–q75 0.6593–0.7889)2,055nm — median 0.6934 (q25–q75 0.6352–0.7685)2,069nm — median 0.6741 (q25–q75 0.6156–0.7513)2,083nm — median 0.6548 (q25–q75 0.5985–0.7336)2,098nm — median 0.6356 (q25–q75 0.5786–0.7146)2,112nm — median 0.6189 (q25–q75 0.5629–0.6965)2,127nm — median 0.6029 (q25–q75 0.5476–0.6802)2,141nm — median 0.5907 (q25–q75 0.5357–0.6677)2,155nm — median 0.5806 (q25–q75 0.5259–0.6569)2,170nm — median 0.5702 (q25–q75 0.517–0.6468)2,184nm — median 0.5615 (q25–q75 0.5094–0.6376)2,199nm — median 0.5537 (q25–q75 0.5022–0.6309)2,213nm — median 0.55 (q25–q75 0.4984–0.6282)2,227nm — median 0.5524 (q25–q75 0.4996–0.6296)2,242nm — median 0.5622 (q25–q75 0.5075–0.6387)2,256nm — median 0.577 (q25–q75 0.5208–0.6525)2,271nm — median 0.5965 (q25–q75 0.5388–0.6707)2,285nm — median 0.615 (q25–q75 0.5568–0.6886)2,299nm — median 0.632 (q25–q75 0.5742–0.7053)2,314nm — median 0.6482 (q25–q75 0.5896–0.7217)2,328nm — median 0.6616 (q25–q75 0.604–0.7355)2,342nm — median 0.675 (q25–q75 0.6173–0.7471)2,357nm — median 0.691 (q25–q75 0.6326–0.7605)2,371nm — median 0.7061 (q25–q75 0.6479–0.775)2,386nm — median 0.7239 (q25–q75 0.6662–0.7925)2,400nm — median 0.7415 (q25–q75 0.6844–0.8084)

Sampling

Wavelengths2,001
Axis range400–2,400 nm
Mean spacing1 nm
Griduniform
Observations1,971

Signal & quality

Value range-0.0259 – 1.06
Mean range0.114 – 0.955
Mean level0.4737
Area947.1
PTP0.8403
Noise RMS1.8354e-05
SNR2.6e+04
SNR dB9e+01 dB
Dynamic range0.84
Smoothness0.0005794
Saturated0.0%
X-outliers898

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count185,011
Spike rate4.70%
Jump count139,263
Jump rate3.53%
Clip fraction0.00%

Shape & reference

Baseline slope0.11752
Curvature RMS0.00056406
D1 RMS0.0032363
RMS to mean0.054105
RMS p950.18191
SAM to mean0.075237
SAM p950.2089
Affine offset p950.23128
Affine gain p95 Δ0.17264
Affine residual p950.081012
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median4.5
Hotelling T2 p95/median9.4
Mahalanobis H p95/median3.1
Repeat groups0

Dimensionality (PCA)

Effective rank1.7
PCs → 95% var2
PCs → 99% var4
Top-10 cum. var99.9%
Computed metric scores 29worst 1.00
FamilleMétrique calculéeValeurScoreNiveauInterprétation datasetCauses typiquesCalcul / scoring
Intégrité des donnéesNaN ratiointegrity.nan_ratio0%0.00faibleSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countintegrity.inf_count00.00faibleNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratiointegrity.zero_ratio0%0.00faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance0.473720.28faibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve947.070.28faibleNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)amplitude.peak_to_peak0.840340.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0859890.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms1.8354e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr258100.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min190.830.00faibleZone fiableDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countartefacts.spike_count185,0111.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate4.7%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count139,2631.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.53%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction5.07e-05%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope0.117520.28faibleStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000564060.07faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00323630.08faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio4.50550.56moyenSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio9.42751.00fortExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio3.07040.77fortOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.181910.87fortSpectre différentDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)reference.sam_to_mean_spectrum_p950.20890.60moyenForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id—0.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id—0.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id—0.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density5.72731.00fortSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p954.52231.00fortSpectre isoléCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.589021.00fortSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-1001020-5.0-2.50.02.55.0PC1 -0.403 · PC2 -0.5943PC1 -0.9191 · PC2 -0.769PC1 -1.116 · PC2 -0.3616PC1 -2.935 · PC2 -0.4435PC1 -2.647 · PC2 0.3885PC1 -2.897 · PC2 0.1862PC1 -2.525 · PC2 -0.1084PC1 -3.281 · PC2 0.09029PC1 -1.807 · PC2 -0.2817PC1 -3.042 · PC2 -0.09859PC1 -1.591 · PC2 -0.709PC1 -1.872 · PC2 -0.2652PC1 -2.064 · PC2 -0.4748PC1 -0.5098 · PC2 -0.717PC1 -1.467 · PC2 0.3727PC1 -1.607 · PC2 0.6934PC1 -2.042 · PC2 0.8258PC1 -3.675 · PC2 1.187PC1 -1.666 · PC2 0.3855PC1 -1.453 · PC2 0.4399PC1 -1.751 · PC2 0.349PC1 -3.943 · PC2 0.6963PC1 -3.879 · PC2 0.5784PC1 -2.646 · PC2 0.5761PC1 -2.585 · PC2 0.2459PC1 -0.7675 · PC2 -0.4051PC1 -0.9538 · PC2 -0.5817PC1 -0.6479 · PC2 -0.6315PC1 -1.167 · PC2 -0.4233PC1 -1.087 · PC2 -0.4018PC1 -0.6371 · PC2 -0.5813PC1 -1.58 · PC2 -0.2PC1 -1.916 · PC2 -0.3747PC1 -2.871 · PC2 0.07482PC1 -2.169 · PC2 0.1094PC1 -2.964 · PC2 0.5927PC1 -2.385 · PC2 0.7143PC1 -2.535 · PC2 0.6444PC1 -1.645 · PC2 -0.6129PC1 -1.786 · PC2 -0.5637PC1 -1.158 · PC2 -0.781PC1 -1.915 · PC2 -0.4844PC1 -0.01348 · PC2 -0.9488PC1 -2.245 · PC2 0.5365PC1 -1.097 · PC2 -0.1061PC1 -0.8262 · PC2 -0.4115PC1 -2.436 · PC2 -0.2324PC1 -1.786 · PC2 0.1727PC1 -1.316 · PC2 -0.07154PC1 -0.7379 · PC2 -0.5722PC1 -2.297 · PC2 -0.4322PC1 -1.482 · PC2 -0.2818PC1 -0.9841 · PC2 -0.2845PC1 -1.408 · PC2 -0.5179PC1 -2.403 · PC2 0.1147PC1 -2.038 · PC2 -0.06593PC1 -3.817 · PC2 1.168PC1 -3.732 · PC2 1.021PC1 -2.616 · PC2 0.932PC1 -2.703 · PC2 0.679PC1 -2.534 · PC2 0.5239PC1 -3.24 · PC2 0.04435PC1 -3.095 · PC2 0.3044PC1 0.1815 · PC2 -0.8766PC1 -0.147 · PC2 -0.8445PC1 -0.6913 · PC2 -0.3059PC1 -1.268 · PC2 -0.3661PC1 0.7457 · PC2 -0.7594PC1 0.2739 · PC2 -0.6725PC1 -0.4903 · PC2 -0.4958PC1 -2.42 · PC2 0.5353PC1 -3.96 · PC2 0.7918PC1 -0.7218 · PC2 0.4107PC1 -1.284 · PC2 -0.1841PC1 -0.01685 · PC2 -0.4332PC1 -2.317 · PC2 0.2592PC1 -1.142 · PC2 0.2746PC1 -0.6765 · PC2 -0.4369PC1 -0.469 · PC2 -0.6775PC1 0.6812 · PC2 -0.8425PC1 -0.09596 · PC2 -0.5316PC1 -0.1419 · PC2 -0.4423PC1 -2.334 · PC2 0.4703PC1 -2.21 · PC2 0.4324PC1 -1.318 · PC2 0.07388PC1 -1.86 · PC2 -0.02184PC1 -1.967 · PC2 0.2041PC1 -2.734 · PC2 0.3851PC1 -0.6294 · PC2 -0.6713PC1 0.9417 · PC2 -1.021PC1 0.08498 · PC2 -0.7691PC1 -0.2003 · PC2 -0.6745PC1 -1.259 · PC2 0.1537PC1 -1.228 · PC2 0.1384PC1 -3.259 · PC2 0.8117PC1 -2.497 · PC2 0.1561PC1 -3.082 · PC2 0.6072PC1 -1.004 · PC2 -0.1311PC1 -0.4356 · PC2 -0.4063PC1 0.02159 · PC2 -0.4085PC1 -0.6786 · PC2 0.02195PC1 0.365 · PC2 -0.1569PC1 -0.7781 · PC2 0.1187PC1 -1.121 · PC2 0.02726PC1 -1.009 · PC2 -0.09579PC1 -2.884 · PC2 0.1889PC1 -1.225 · PC2 -0.5121PC1 -3.24 · PC2 0.386PC1 -3.165 · PC2 0.3163PC1 -3.302 · PC2 0.5635PC1 -0.6308 · PC2 -0.5949PC1 -2.744 · PC2 0.2441PC1 0.7748 · PC2 -0.8211PC1 -2.578 · PC2 0.4015PC1 -3.866 · PC2 0.6933PC1 -3.046 · PC2 0.693PC1 -3.274 · PC2 0.6638PC1 -2.504 · PC2 0.1398PC1 -0.7433 · PC2 -0.248PC1 -0.4481 · PC2 -0.09748PC1 -1.395 · PC2 -0.01578PC1 -1.444 · PC2 0.03022PC1 -2.191 · PC2 0.544PC1 -1.838 · PC2 0.2844PC1 -1.868 · PC2 -0.1504PC1 -1.166 · PC2 -0.03605PC1 -1.067 · PC2 0.2701PC1 -1.848 · PC2 0.2916PC1 -3.355 · PC2 0.7252PC1 -3.155 · PC2 0.753PC1 -2.509 · PC2 0.4803PC1 -1.163 · PC2 -0.1267PC1 -0.7791 · PC2 0.1839PC1 -1.585 · PC2 0.08657PC1 -1.878 · PC2 0.2075PC1 -2.208 · PC2 0.5289PC1 -2.886 · PC2 0.6806PC1 -0.6999 · PC2 0.01374PC1 -2.519 · PC2 0.1165PC1 -0.9909 · PC2 0.09806PC1 -1.09 · PC2 -0.287PC1 -0.1154 · PC2 -0.7775PC1 -2.625 · PC2 0.2287PC1 0.04839 · PC2 -0.201PC1 0.1389 · PC2 -0.4618PC1 0.003742 · PC2 -0.03795PC1 0.07184 · PC2 -0.4447PC1 -5.569 · PC2 1.783PC1 -3.071 · PC2 0.4764PC1 -2.381 · PC2 0.7288PC1 -1.019 · PC2 0.1989PC1 0.1686 · PC2 -0.6995PC1 -0.6165 · PC2 -0.5572PC1 -2.278 · PC2 -0.1059PC1 -1.669 · PC2 -0.3535PC1 -3.931 · PC2 0.4888PC1 -0.0241 · PC2 -0.5252PC1 -0.1874 · PC2 -0.7003PC1 0.2601 · PC2 -0.818PC1 -2.905 · PC2 0.9337PC1 -2.913 · PC2 0.5239PC1 -4.213 · PC2 1.361PC1 -2.51 · PC2 0.06569PC1 0.5045 · PC2 -0.8274PC1 0.5415 · PC2 -0.8183PC1 -2.35 · PC2 0.6092PC1 -1.923 · PC2 0.8163PC1 -2.468 · PC2 0.3703PC1 -3.947 · PC2 1.143PC1 -3.368 · PC2 0.4396PC1 -0.503 · PC2 -0.6106PC1 0.5249 · PC2 -0.6591PC1 -1.563 · PC2 0.01032PC1 -0.2007 · PC2 -0.821PC1 0.3424 · PC2 -0.8046PC1 -0.8095 · PC2 -0.6837PC1 0.1852 · PC2 -1.176PC1 8.674 · PC2 2.498PC1 8.829 · PC2 1.527PC1 8.472 · PC2 3.891PC1 -2.531 · PC2 -0.254PC1 -4.806 · PC2 0.9091PC1 -3.808 · PC2 0.6539PC1 0.3466 · PC2 -1.557PC1 7.375 · PC2 0.6316PC1 9.647 · PC2 2.448PC1 -6.791 · PC2 1.448PC1 -6.372 · PC2 1.249PC1 -4.572 · PC2 0.5595PC1 -7.862 · PC2 2.087PC1 -2.519 · PC2 -0.514PC1 -1.241 · PC2 -1.065PC1 -4.148 · PC2 0.02472PC1 -3.141 · PC2 0.3831PC1 -5.669 · PC2 0.989PC1 -3.45 · PC2 0.2379PC1 7.355 · PC2 1.892PC1 8.489 · PC2 1.751PC1 -2.125 · PC2 -0.6254PC1 -1.404 · PC2 -0.3737PC1 -1.434 · PC2 -0.3945PC1 -4.244 · PC2 0.448PC1 -2.307 · PC2 -0.4077PC1 -4.113 · PC2 0.5369PC1 -2.803 · PC2 -0.186PC1 -1.162 · PC2 -0.3336PC1 -0.4462 · PC2 -1.931PC1 -2.765 · PC2 0.1335PC1 -1.644 · PC2 -0.5365PC1 -2.673 · PC2 -0.2309PC1 7.147 · PC2 2.696PC1 7.978 · PC2 3.022PC1 10.37 · PC2 2.401PC1 9.806 · PC2 2.04PC1 -0.8433 · PC2 -0.1918PC1 -3.308 · PC2 0.2996PC1 -4.402 · PC2 0.8938PC1 -1.581 · PC2 -0.3743PC1 -2.264 · PC2 -0.1599PC1 -4.91 · PC2 1.173PC1 -0.9119 · PC2 -1.006PC1 -0.3875 · PC2 -0.8698PC1 -0.6748 · PC2 -0.6235PC1 -2.943 · PC2 0.4987PC1 -2.962 · PC2 0.814PC1 -3.965 · PC2 1.156PC1 -1.178 · PC2 -0.5246PC1 -2.582 · PC2 0.1087PC1 -3.356 · PC2 0.5929PC1 -2.543 · PC2 0.1952PC1 -0.1143 · PC2 -0.5991PC1 0.02502 · PC2 -0.4729PC1 -1.617 · PC2 0.1253PC1 0.3399 · PC2 -0.7517PC1 -1.776 · PC2 -0.0519PC1 -2.187 · PC2 0.221PC1 -2.299 · PC2 0.137PC1 -1.136 · PC2 -0.2604PC1 -3.089 · PC2 0.7016PC1 0.1186 · PC2 -0.8283PC1 -3.233 · PC2 0.6963PC1 0.1389 · PC2 -0.8433PC1 -5.043 · PC2 1.776PC1 -2.265 · PC2 0.0328PC1 -2.213 · PC2 0.2252PC1 -5.398 · PC2 1.311PC1 -9.436 · PC2 0.7052PC1 -5.739 · PC2 1.426PC1 -1.451 · PC2 -0.351PC1 -2.033 · PC2 -0.09736PC1 -2.53 · PC2 -0.3455PC1 -0.9628 · PC2 -1.003PC1 -1.628 · PC2 -0.454PC1 -4.93 · PC2 0.7562PC1 -3.499 · PC2 1.085PC1 -3.525 · PC2 0.8322PC1 -4.589 · PC2 1.283PC1 -1.71 · PC2 0.4764PC1 -3.658 · PC2 1.006PC1 -5.188 · PC2 0.4455PC1 -4.659 · PC2 1.68PC1 -3.531 · PC2 0.7279PC1 -1.937 · PC2 -0.06067PC1 -5.568 · PC2 1.922PC1 -1.419 · PC2 -0.5385PC1 -4.644 · PC2 1.114PC1 -1.064 · PC2 -0.4873PC1 -2.424 · PC2 -0.03026PC1 -4.253 · PC2 0.4659PC1 -4.87 · PC2 0.9608PC1 -5.46 · PC2 1.607PC1 -2.445 · PC2 0.6176PC1 -5.3 · PC2 1.229PC1 -5.152 · PC2 1.965PC1 -5.445 · PC2 1.986PC1 -4.609 · PC2 1.119PC1 -5.183 · PC2 1.297PC1 -3.965 · PC2 0.9949PC1 -6.148 · PC2 1.706PC1 -4.753 · PC2 2.021PC1 -5.075 · PC2 1.669PC1 -2.473 · PC2 0.9429PC1 -3.592 · PC2 0.8152PC1 -1.564 · PC2 -0.2222PC1 0.6204 · PC2 -0.7483PC1 -0.8182 · PC2 -0.2669PC1 6.691 · PC2 1.843PC1 4.532 · PC2 1.363PC1 -2.078 · PC2 0.3441PC1 0.3524 · PC2 -1.102PC1 6.491 · PC2 1.507PC1 7.732 · PC2 2.376PC1 7.283 · PC2 2.56PC1 -3.261 · PC2 1.178PC1 9.864 · PC2 1.889PC1 -1.79 · PC2 0.4613PC1 -1.943 · PC2 0.2096PC1 -0.06588 · PC2 -0.08483PC1 7.485 · PC2 -0.4299PC1 9.043 · PC2 1.361PC1 8.228 · PC2 1.228PC1 10.68 · PC2 3.036PC1 -3.742 · PC2 0.8769PC1 -2.676 · PC2 0.3887PC1 -3.164 · PC2 0.5939PC1 -2.267 · PC2 -0.006294PC1 -2.334 · PC2 0.3992PC1 0.3068 · PC2 -0.2777PC1 -7.071 · PC2 2.562PC1 -2.797 · PC2 0.3989PC1 -4.301 · PC2 1.381PC1 -6.352 · PC2 2.328PC1 -5.27 · PC2 2.35PC1 -5.136 · PC2 2.111PC1 -4.299 · PC2 1.835PC1 -7.997 · PC2 3.088PC1 10.29 · PC2 1.509PC1 10.36 · PC2 1.788PC1 11.26 · PC2 2.62PC1 8.307 · PC2 1.755PC1 9.164 · PC2 1.519PC1 9.21 · PC2 1.082PC1 10.54 · PC2 2.373PC1 8.925 · PC2 1.432PC1 9.57 · PC2 1.972PC1 10.73 · PC2 2.179PC1 -1.348 · PC2 0.167PC1 -1.363 · PC2 -0.3298PC1 2.359 · PC2 -1.383PC1 0.7888 · PC2 -1.332PC1 -0.9373 · PC2 -0.4206PC1 0.0294 · PC2 -1.027PC1 0.7443 · PC2 -1.2PC1 2.855 · PC2 -1.575PC1 -1.409 · PC2 -0.5237PC1 2.554 · PC2 -0.3124PC1 0.2071 · PC2 1.412PC1 1.092 · PC2 1.012PC1 -0.411 · PC2 -0.5018PC1 0.344 · PC2 -1.227PC1 -0.146 · PC2 -0.9912PC1 -0.4711 · PC2 -0.5784PC1 -2.964 · PC2 0.2753PC1 -2.008 · PC2 -0.03129PC1 1.199 · PC2 1.096PC1 -0.7918 · PC2 -0.635PC1 1.462 · PC2 1.469PC1 2.131 · PC2 -0.5073PC1 -1.358 · PC2 -0.6354PC1 2.276 · PC2 -1.09PC1 -1.943 · PC2 0.07682PC1 1.134 · PC2 -1.63PC1 0.9641 · PC2 -0.858PC1 -1.722 · PC2 -0.05277PC1 -1.638 · PC2 -0.4667PC1 -0.7221 · PC2 -0.6773PC1 -1.581 · PC2 -0.3206PC1 -1.914 · PC2 -0.2682PC1 -1.795 · PC2 -0.1003PC1 -1.261 · PC2 -0.7059PC1 -0.9941 · PC2 -0.2961PC1 -0.6095 · PC2 -1.223PC1 -1.95 · PC2 -1.143PC1 -3.555 · PC2 -0.04326PC1 1.921 · PC2 -1.997PC1 1.325 · PC2 -2.001PC1 -0.313 · PC2 -1.107PC1 1.392 · PC2 -1.267PC1 2.107 · PC2 -1.967PC1 0.2557 · PC2 -1.513PC1 -2.125 · PC2 -0.7985PC1 -0.9337 · PC2 -0.2054PC1 -3.345 · PC2 2.133PC1 6.172 · PC2 -1.204PC1 5.539 · PC2 -1.094PC1 2.543 · PC2 -1.766PC1 2.935 · PC2 -1.979PC1 -3.628 · PC2 0.014PC1 -0.156 · PC2 -1.104PC1 -0.255 · PC2 -0.9461PC1 -2.01 · PC2 -0.1143PC1 -1.994 · PC2 -0.6409PC1 -1.929 · PC2 -0.3537PC1 -1.368 · PC2 -1.208PC1 -1.451 · PC2 0.2944PC1 0.6192 · PC2 1.767PC1 -2.84 · PC2 -0.01443PC1 5.62 · PC2 -2.17PC1 6.181 · PC2 -1.981PC1 2.69 · PC2 -0.1231PC1 3.145 · PC2 0.2516PC1 -2.377 · PC2 -0.04081PC1 -2.739 · PC2 -0.2664PC1 -2.907 · PC2 0.9519PC1 -3.643 · PC2 -0.05091PC1 -0.9834 · PC2 1.124PC1 -0.08512 · PC2 -1.372PC1 6.576 · PC2 -1.73PC1 6.447 · PC2 -1.439PC1 -3.192 · PC2 -0.6195PC1 -1.266 · PC2 1.405PC1 1.03 · PC2 -1.926PC1 -3.507 · PC2 0.1765PC1 -3.334 · PC2 -0.4068PC1 1.835 · PC2 -1.613PC1 2.185 · PC2 -2.25PC1 1.43 · PC2 -1.439PC1 1.095 · PC2 -1.325PC1 -1.699 · PC2 1.191PC1 0.7136 · PC2 -1.48PC1 -2.474 · PC2 1.203PC1 1.354 · PC2 -1.733PC1 -2.333 · PC2 1.064PC1 0.5297 · PC2 -1.568PC1 1.353 · PC2 -1.425PC1 -3.676 · PC2 -0.127PC1 -3.456 · PC2 0.415PC1 -1.103 · PC2 -0.4067PC1 -1.693 · PC2 -0.4484PC1 6.759 · PC2 -1.049PC1 7.089 · PC2 -0.7099PC1 -4.293 · PC2 1.024PC1 -4.473 · PC2 1.142PC1 4.009 · PC2 -1.716PC1 4.352 · PC2 -1.263PC1 -5.01 · PC2 1.239PC1 -1.65 · PC2 -0.05099PC1 -1.68 · PC2 -0.2678PC1 -1.777 · PC2 -0.2584PC1 -0.5223 · PC2 -0.4946PC1 -0.3712 · PC2 -0.8192PC1 -1.799 · PC2 -0.1118PC1 -1.994 · PC2 -0.4286PC1 -1.204 · PC2 -0.4927PC1 -3.719 · PC2 0.6305PC1 -2.916 · PC2 0.3815PC1 -0.7816 · PC2 -0.6271PC1 -2.109 · PC2 0.06752PC1 -4.139 · PC2 0.6146PC1 -1.631 · PC2 -0.3732PC1 0.1493 · PC2 -0.9313PC1 -2.591 · PC2 0.07756PC1 -0.9531 · PC2 -0.3956PC1 -0.6872 · PC2 -1.005PC1 -0.736 · PC2 -0.7361PC1 -4.682 · PC2 1.401PC1 -4.766 · PC2 1.312PC1 -2.642 · PC2 0.2173PC1 -2.731 · PC2 0.2759PC1 -3.209 · PC2 0.6219PC1 -0.4837 · PC2 -0.359PC1 0.1515 · PC2 -0.8581PC1 4.329 · PC2 -1.094PC1 -2.797 · PC2 0.2408PC1 -1.385 · PC2 -0.5767PC1 0.03632 · PC2 -0.5845PC1 -0.425 · PC2 -0.6883PC1 -0.4209 · PC2 0.04715PC1 -1.937 · PC2 0.7461PC1 -2.872 · PC2 0.7669PC1 -0.5371 · PC2 -0.6117PC1 -2.966 · PC2 0.715PC1 -4.312 · PC2 0.8296PC1 -3.253 · PC2 0.6409PC1 8.404 · PC2 1.17PC1 6.978 · PC2 0.4597PC1 7.391 · PC2 0.3097PC1 -3.988 · PC2 0.9034PC1 -2.694 · PC2 0.5793PC1 -2.073 · PC2 0.5098PC1 -2.742 · PC2 0.8103PC1 0.3365 · PC2 -0.2927PC1 6.289 · PC2 1.717PC1 -3.831 · PC2 0.9396PC1 12.24 · PC2 3.497PC1 6.976 · PC2 0.4197PC1 -2.908 · PC2 0.7347PC1 -1.167 · PC2 0.07691PC1 -1.231 · PC2 -0.1871PC1 -1.205 · PC2 -0.7553PC1 -2.105 · PC2 0.3753PC1 -3.399 · PC2 0.3709PC1 -4.833 · PC2 1.314PC1 -0.6197 · PC2 -0.795PC1 -3.742 · PC2 0.5113PC1 0.03194 · PC2 -0.7707PC1 -0.6542 · PC2 -0.2685PC1 -1.656 · PC2 -0.2664PC1 2.975 · PC2 2.376PC1 -1.374 · PC2 -0.2296PC1 -4.363 · PC2 0.7088PC1 -3.168 · PC2 0.1983PC1 -2.412 · PC2 0.3709PC1 -1.222 · PC2 -0.5823PC1 -2.245 · PC2 -0.06098PC1 -2.173 · PC2 -0.086PC1 -1.934 · PC2 0.08129PC1 -4.523 · PC2 1.373PC1 11.09 · PC2 2.873PC1 10.71 · PC2 2.154PC1 -3.849 · PC2 0.007746PC1 10.1 · PC2 1.966PC1 7.44 · PC2 0.4184PC1 10.39 · PC2 0.5639PC1 -3.721 · PC2 -0.01257PC1 11.94 · PC2 2.038PC1 5.805 · PC2 1.866PC1 -2.698 · PC2 0.6129PC1 -4.201 · PC2 0.4822PC1 -4.852 · PC2 0.9207PC1 -0.9684 · PC2 -0.6672PC1 -0.8938 · PC2 0.08316PC1 -4.933 · PC2 0.9878PC1 0.8616 · PC2 -0.5381PC1 -3.433 · PC2 0.8119PC1 -3.259 · PC2 0.4372PC1 -1.906 · PC2 -0.4735PC1 -1.591 · PC2 0.701PC1 -2.627 · PC2 0.7822PC1 -4.301 · PC2 1.09PC1 8.058 · PC2 0.3389PC1 11.61 · PC2 2.271PC1 7.736 · PC2 0.3007PC1 11.05 · PC2 2.005PC1 -4.722 · PC2 0.705PC1 8.57 · PC2 0.3197PC1 11.03 · PC2 0.7002PC1 10.81 · PC2 1.026PC1 4.436 · PC2 1.527PC1 1.039 · PC2 -1.7PC1 -0.6188 · PC2 -0.7155PC1 -1.367 · PC2 -0.8738PC1 7.409 · PC2 -0.1462PC1 0.9298 · PC2 -1.917PC1 1.187 · PC2 -1.448PC1 0.4903 · PC2 -1.462PC1 2.442 · PC2 -1.291PC1 3.119 · PC2 -1.935PC1 2.338 · PC2 -1.239PC1 3.536 · PC2 -1.577PC1 1.455 · PC2 -1.902PC1 1.891 · PC2 -0.4646PC1 5.504 · PC2 2.544PC1 4.647 · PC2 2.374PC1 3.11 · PC2 -1.924PC1 2.06 · PC2 -1.391PC1 4.877 · PC2 1.514PC1 5.58 · PC2 2.373PC1 2.008 · PC2 -1.359PC1 2.513 · PC2 1.685PC1 2.405 · PC2 -1.446PC1 3.906 · PC2 2.109PC1 2.902 · PC2 -1.813PC1 2.987 · PC2 -1.423PC1 4.556 · PC2 2.162PC1 0.07001 · PC2 -1.07PC1 1.257 · PC2 -1.388PC1 2.394 · PC2 -1.267PC1 2.147 · PC2 -2.097PC1 5.385 · PC2 1.386PC1 2.317 · PC2 -1.142PC1 5.24 · PC2 2.233PC1 -1.026 · PC2 0.2529PC1 1.649 · PC2 -0.778PC1 2.86 · PC2 -1.143PC1 6.175 · PC2 0.9384PC1 3.329 · PC2 -1.015PC1 6.437 · PC2 2.402PC1 2.307 · PC2 -0.8258PC1 6.654 · PC2 2.305PC1 3.552 · PC2 -0.1402PC1 6.137 · PC2 2.94PC1 0.13 · PC2 -0.7625PC1 2.98 · PC2 -1.279PC1 1.61 · PC2 -0.7086PC1 5.18 · PC2 -0.4594PC1 2.776 · PC2 -1.499PC1 3.117 · PC2 -0.7861PC1 2.427 · PC2 -1.428PC1 0.975 · PC2 -1.04PC1 1.276 · PC2 -0.9859PC1 1.988 · PC2 -0.7937PC1 4.747 · PC2 -0.9639PC1 4.455 · PC2 -1.253PC1 3.445 · PC2 -1.905PC1 2.456 · PC2 -2.352PC1 1.047 · PC2 -1.543PC1 1.136 · PC2 -2.059PC1 3.074 · PC2 -1.962PC1 10.21 · PC2 3.734PC1 0.7741 · PC2 1.088PC1 1.045 · PC2 1.307PC1 6.626 · PC2 1.83PC1 8.618 · PC2 2.506PC1 -0.1302 · PC2 0.4568PC1 5.406 · PC2 0.883PC1 7.796 · PC2 2.413PC1 7.095 · PC2 1.873PC1 5.454 · PC2 3.316PC1 5.578 · PC2 3.286PC1 4.869 · PC2 3.519PC1 5.785 · PC2 3.797PC1 0.3916 · PC2 4.132PC1 3.221 · PC2 4.924PC1 3.824 · PC2 2.853PC1 2.424 · PC2 3.002PC1 -3.46 · PC2 2.341PC1 -4.427 · PC2 2.356PC1 -0.7109 · PC2 -0.5143PC1 0.3704 · PC2 0.4611PC1 -1.271 · PC2 -0.7088PC1 4.099 · PC2 -3.988PC1 -1.067 · PC2 -0.2506PC1 -0.7728 · PC2 0.01719PC1 -0.6792 · PC2 -0.687PC1 -1.348 · PC2 0.3262PC1 -0.1282 · PC2 -0.417PC1 0.3907 · PC2 -1.91PC1 -0.4112 · PC2 -1.416PC1 0.1465 · PC2 -1.478PC1 0.1469 · PC2 -1.627PC1 -0.8835 · PC2 -1.223PC1 2.129 · PC2 -2.497PC1 3.721 · PC2 -1.762PC1 2.915 · PC2 -2.296PC1 0.5745 · PC2 -1.881PC1 2.47 · PC2 -2.251PC1 -4.549 · PC2 0.5283PC1 -5.071 · PC2 0.8711PC1 -3.056 · PC2 0.4412PC1 -2.842 · PC2 0.2708PC1 -2.877 · PC2 -0.1412PC1 -2.827 · PC2 0.3306PC1 -3.632 · PC2 0.6604PC1 -4.26 · PC2 0.7558PC1 -2.561 · PC2 0.04945PC1 -0.1834 · PC2 -1.396PC1 1.441 · PC2 -1.988PC1 -3.187 · PC2 0.4994PC1 -3.538 · PC2 0.6897PC1 -3.67 · PC2 0.5986PC1 -4.499 · PC2 0.8471PC1 -3.115 · PC2 0.5637PC1 -0.7514 · PC2 0.09297PC1 -1.01 · PC2 0.8874PC1 -0.7637 · PC2 0.1431PC1 -1.07 · PC2 0.1227PC1 1.141 · PC2 0.4322PC1 -1.46 · PC2 1.334PC1 -0.4638 · PC2 -0.9306PC1 -1.175 · PC2 -1.475PC1 -0.3803 · PC2 -0.38PC1 0.6619 · PC2 -1.076PC1 0.6344 · PC2 -0.6905PC1 -1.302 · PC2 -0.6965PC1 -1.266 · PC2 -0.4887PC1 -0.6588 · PC2 -0.8152PC1 -1.073 · PC2 -0.3868PC1 0.3151 · PC2 -0.8625PC1 1.722 · PC2 -1.229PC1 0.1693 · PC2 -0.5729PC1 -0.3787 · PC2 -1.099PC1 1.128 · PC2 -1.341PC1 0.6619 · PC2 -1.209PC1 1.326 · PC2 -1.162PC1 1.403 · PC2 -1.343PC1 1.7 · PC2 -1.373PC1 1.466 · PC2 -0.6366PC1 -1.229 · PC2 0.3892PC1 0.2897 · PC2 -0.3703PC1 -2.109 · PC2 0.3836PC1 -1.125 · PC2 0.1853PC1 7.814 · PC2 -1.315PC1 6.743 · PC2 -1.177PC1 7.822 · PC2 -1.164PC1 0.05276 · PC2 -0.9063PC1 -0.284 · PC2 -0.8719PC1 0.458 · PC2 -0.9944PC1 -0.456 · PC2 -0.7405PC1 0.1166 · PC2 -1.012PC1 0.9336 · PC2 -1.261PC1 0.9223 · PC2 -1.166PC1 -1.229 · PC2 -0.869PC1 -1.125 · PC2 -0.978PC1 -2.587 · PC2 -0.7592PC1 -1.932 · PC2 -0.8755PC1 -2.754 · PC2 -0.4722PC1 -2.057 · PC2 -0.3985PC1 -1.869 · PC2 -0.7644PC1 -1.092 · PC2 0.06433PC1 -0.2819 · PC2 -0.1921PC1 7.927 · PC2 -1.198PC1 8.708 · PC2 -0.7933PC1 7.883 · PC2 -1.101PC1 7.933 · PC2 -0.8538PC1 8.047 · PC2 -0.9233PC1 1.046 · PC2 -1.287PC1 -1.602 · PC2 -0.3005PC1 -0.8549 · PC2 -0.4424PC1 -2.104 · PC2 0.000135PC1 -0.1335 · PC2 -0.8753PC1 -0.4143 · PC2 -0.854PC1 0.2148 · PC2 -1.202PC1 0.6353 · PC2 -1.346PC1 0.3147 · PC2 -1.158PC1 -2.625 · PC2 -0.4685PC1 -3.865 · PC2 -0.1952PC1 -3.458 · PC2 -0.239PC1 -3.526 · PC2 -0.2932PC1 -3.367 · PC2 -0.1618PC1 -2.191 · PC2 0.2798PC1 -0.2999 · PC2 -0.3879PC1 6.317 · PC2 -1.748PC1 6.694 · PC2 -1.58PC1 5.792 · PC2 -1.499PC1 -1.175 · PC2 -0.2482PC1 -1.119 · PC2 -0.1201PC1 -1.552 · PC2 -0.003863PC1 0.3291 · PC2 -0.2924PC1 -0.1347 · PC2 -0.4164PC1 -0.4162 · PC2 -0.6221PC1 0.262 · PC2 -1.115PC1 -0.02006 · PC2 -0.5731PC1 0.7843 · PC2 -0.9477PC1 -1.579 · PC2 -0.7113PC1 -2.288 · PC2 -0.3721PC1 -1.944 · PC2 -0.5735PC1 -2.066 · PC2 -0.3771PC1 -1.618 · PC2 0.4709PC1 -0.9487 · PC2 0.7721PC1 -1.264 · PC2 0.7868PC1 -0.3293 · PC2 -0.1009PC1 -0.9693 · PC2 0.561PC1 6.814 · PC2 -1.564PC1 6.018 · PC2 -1.524PC1 5.906 · PC2 -1.477PC1 7.011 · PC2 -1.314PC1 -1.291 · PC2 -0.2188PC1 -0.1011 · PC2 -0.4156PC1 -1.984 · PC2 -0.009701PC1 -1.591 · PC2 -0.195PC1 -1.427 · PC2 -0.6053PC1 -2.189 · PC2 -0.3032PC1 -2.456 · PC2 -0.3182PC1 -1.647 · PC2 -0.4949PC1 -2.81 · PC2 -0.1309PC1 -1.771 · PC2 -0.4615PC1 1.187 · PC2 -1.004PC1 1.724 · PC2 -0.9877PC1 0.9165 · PC2 -0.902PC1 -0.6544 · PC2 0.5512PC1 -1.605 · PC2 1.052PC1 -0.6304 · PC2 1.411PC1 -1.076 · PC2 1.286PC1 6.194 · PC2 -1.313PC1 5.364 · PC2 -1.35PC1 6.193 · PC2 -1.408PC1 4.813 · PC2 -1.266PC1 5.886 · PC2 -1.416PC1 -0.5155 · PC2 -0.3554PC1 -0.4594 · PC2 -0.541PC1 -1.157 · PC2 -0.2035PC1 -0.8766 · PC2 -0.2737PC1 0.569 · PC2 -0.5077PC1 -1.84 · PC2 -0.456PC1 -1.515 · PC2 -0.7412PC1 -1.537 · PC2 -0.7026PC1 -0.4412 · PC2 0.7044PC1 -0.9056 · PC2 0.9748PC1 6.999 · PC2 -1.224PC1 5.404 · PC2 -1.355PC1 6.557 · PC2 -0.7213PC1 6.288 · PC2 -1.331PC1 0.4128 · PC2 -0.9514PC1 -0.1349 · PC2 -0.834PC1 -0.2347 · PC2 -0.7643PC1 0.5923 · PC2 -0.7753PC1 1.748 · PC2 -1.426PC1 1.33 · PC2 -1.411PC1 2.846 · PC2 -0.1788PC1 4.763 · PC2 -0.5872PC1 2.763 · PC2 -0.656PC1 2.597 · PC2 -0.3509PC1 3.757 · PC2 -0.8534PC1 3.798 · PC2 -0.5493PC1 2.565 · PC2 -1.117PC1 3.517 · PC2 -0.6291PC1 4.133 · PC2 -0.7609PC1 3.293 · PC2 -0.2952PC1 3.397 · PC2 -0.2268PC1 2.092 · PC2 0.2886PC1 3.911 · PC2 0.3358PC1 4.933 · PC2 -0.7177PC1 4.282 · PC2 -1.287PC1 4.767 · PC2 -0.5216PC1 3.9 · PC2 -1.022PC1 3.744 · PC2 -1.035PC1 4.047 · PC2 -1.243PC1 4.025 · PC2 -1.475PC1 5.866 · PC2 -1.447PC1 (87.4%)PC2 (8.1%)800 scores
PCA explained variance0%25%50%75%100%PC1: 87.6% (cumulative 87.6%)1PC2: 8.0% (cumulative 95.6%)2PC3: 3.0% (cumulative 98.6%)3PC4: 0.6% (cumulative 99.2%)4PC5: 0.4% (cumulative 99.6%)5PC6: 0.2% (cumulative 99.7%)6PC7: 0.1% (cumulative 99.8%)7PC8: 0.1% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 99.9%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 20
X · SLA spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · LDMC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · Cmass spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
SLA0.6095300.4322.8%
LDMC0.3645160.1380.0%
Cmass0.4745090.2240.0%
Nmass0.4031,7120.2850.0%
solubles_mass0.3494330.1160.0%
hemicellulose_mass0.4384330.05410.0%
cellulose_mass0.3824360.1980.0%
lignin_mass0.3945050.07980.0%
chlA_mass0.3642,2960.2610.0%
chlB_mass0.3792,2970.2630.0%
car_mass0.371,7150.2690.0%
Al_mass0.3134820.0970.0%
B208.9_mass0.1494000.07640.0%
B249.8_mass0.1464000.07280.0%
Ca_mass0.267440.1550.0%
Cu_mass0.4041,7050.2690.0%
Fe_mass0.4651,7040.3150.0%
K_mass0.3075200.1350.0%
Mg_mass0.3071,2040.2410.0%
Mn_mass0.2164030.0360.0%

Metric interpretation reference

Metric catalog 29
FamilleMétriqueCe qu’elle détecteForte valeur =Faible valeur =Causes typiquesCalcul / score
Intégrité des donnéesNaN ratioDonnées manquantesSpectre corrompuSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countValeurs infiniesCorruptionNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratioColonnes ou cellules nullesSpectre tronquéNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceNiveau moyenTrop clair / fond visibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveIntensité globaleDifférence d'éclairementNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)DynamiqueVariabilité forteSpectre platSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceVariabilité spectraleNormal ou hétérogèneSpectre platMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSBruit haute fréquenceBruitéStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRQualité signalBon signalMauvais signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRBruit localiséZone fiableZone problématiqueDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countPics étroitsArtefactsSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateDensité de picsSpectre suspectNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countDiscontinuitésRaccord détecteurContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateFréquence de sautsProblème spectralNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionSaturationClippingNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopePente globaleDériveStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSCourbureForme inhabituelleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSVariabilité localeSpectre structuréPlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)Non expliqué par PCASpectre atypiqueConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²Extrême dans PCAExtrême mais cohérentCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis HDistance au nuageOutlier globalPopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumDistance moyenneSpectre différentTypiqueDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)Différence de formeForme différenteSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDReproductibilitéMauvaise répétabilitéStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDVariation de formeInstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDVariabilité interneMauvais contrôleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densityClustersSous-populationsHomogèneLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)Anomalie localeSpectre isoléPopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scoreAnomalie globaleSpectre atypiqueNormalDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions
TechnologieAdaptations / métriquesAnomalies cibléesCommentaire pratique
UV-Vis 300-1000 nmBaseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zonesLumière parasite, mauvais blanc, saturation, faible signal aux extrémitésLes bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nmSaturation / clipping proche absorbance max ou réflectance maxSignal écrêtéTrès important si absorption forte.
UV-Vis 300-1000 nmRed-edge, position de maximum, ratios de bandes si végétalDécalage biologique ou artefact optiqueAide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nmSmoothness / roughness indexBruit haute fréquenceSouvent plus informatif que le SNR seul.
MIR / ATR-FTIRATR contact quality index: intensité globale, aire totale, profondeur des bandes clésMauvais contact cristal-échantillonCrucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsMauvaise correction atmosphériquePics parasites fréquents.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, dérive baselineTrès utile avant PCA.
MIR / ATR-FTIRPeak position shiftMauvais alignement spectral / calibrationImportant en FTIR car de petits shifts comptent.
MIR / ATR-FTIRBand area ratios sur bandes connuesSpectre chimiquement incohérentÀ adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Injection faible, ionisation instableÉquivalent MS du niveau global spectral.
HS-MSNombre de pics détectésSpectre pauvre ou trop bruitéTrop peu = mauvais signal; trop = bruit/contamination.
HS-MSMass accuracy / m/z driftProblème calibration masseFondamental en HRMS.
HS-MSRetention time drift si LC/GC-MSDérive chromatographiqueÀ suivre sur standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overComparer échantillons vs blancs.
HS-MSInternal standard CVVariabilité instrumentaleTrès robuste si standards disponibles.
HS-MSMissingness par featureInstabilité de détectionCrucial pour filtrer les variables.
Avec répétitionsRMS intra-échantillonRépétabilité globaleApplicable à toutes les technologies.
Avec répétitionsSAM / corrélation intra-échantillonRépétabilité de formeTrès utile pour spectres.
Avec répétitionsCV intra-échantillon par bande / featureRépétabilité localeDétecte les zones instables.
Avec répétitionsICC ou variance componentsPart variance échantillon vs techniqueTrès utile si plusieurs répétitions par sample.
Avec répétitionsDistance au centroïde intra-IDRépétition aberrantePermet de flagger la mauvaise répétition plutôt que le sample entier.
Bug-hunting / supervised audits
Famille de bug potentielMéthodes à ajouterCe que ça détecteÉtat dans l’explorateur
Shift spectral globalCorrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de picsDécalage en longueur d'onde, mauvais alignement, interpolation différentePartiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gainRégression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduelOffset additif, effet multiplicatif, dérive de baselineCalculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-YVérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérentsLignes mélangées, metadata mal alignées, Y attribué au mauvais spectrePartiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitéesGroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_idPerformance artificiellement bonne due aux répétitionsNécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugsÉchantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FNY inversés, erreurs de saisie, classes ambiguësNécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachésPCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operatorLots, campagnes, sondes, backgrounds non renseignésPartiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnusCarte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'ondeRégions spectrales anormales non anticipéesÀ calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentalesDiscontinuités dans dérivées, changepoint detectionSplice, raccord détecteur, saut local non prévuCalculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectraleNMF / unmixing / reconstruction par convex hullComposante externe: fond, plastique, solNon calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictivesImportance modèle vs instabilité QC par variableModèle qui apprend un artefact plutôt qu'un signal biologiqueNécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 32

species

target · categorical
species classesPopulus tremuloides MichauxPopulus tremuloides Michaux: 120120Betula populifolia MarshallBetula populifolia Marshall: 104104Acer rubrum LinnaeusAcer rubrum Linnaeus: 9090Acer saccharum MarshallAcer saccharum Marshall: 8181Phalaris arundinacea LinnaeusPhalaris arundinacea Linnaeus: 7575Agonis flexuosa (Willd.) SweetAgonis flexuosa (Willd.) Sweet: 6868Bromus inermis LeysserBromus inermis Leysser: 6060Typha LinnaeusTypha Linnaeus: 5656Solidago LinnaeusSolidago Linnaeus: 4949Fagus grandifolia EhrhartFagus grandifolia Ehrhart: 4747+10 more+10 more: 296296
n / missing1,971 / 0
Classes111
Balance (entropy)0.91
Imbalance ratio120
Top classPopulus tremuloides Michaux (120)

latin.genus

target · categorical
latin.genus classesAcerAcer: 265265PopulusPopulus: 183183BetulaBetula: 159159SolidagoSolidago: 9393PhragmitesPhragmites: 7878BromusBromus: 7676QuercusQuercus: 7575PhalarisPhalaris: 7575TyphaTypha: 7373AgonisAgonis: 6868+10 more+10 more: 314314
n / missing1,971 / 0
Classes66
Balance (entropy)0.85
Imbalance ratio9e+01
Top classAcer (265)

latin.species

target · categorical
latin.species classestremuloidestremuloides: 120120LinnaeusLinnaeus: 112112populifoliapopulifolia: 104104rubrumrubrum: 9090saccharumsaccharum: 8181australisaustralis: 7878arundinaceaarundinacea: 7575flexuosaflexuosa: 6868inermisinermis: 6060americanaamericana: 4848+10 more+10 more: 315315
n / missing1,971 / 0
Classes94
Balance (entropy)0.9
Imbalance ratio120
Top classtremuloides (120)

growth.form

target · categorical
growth.form classestreetree: 1,1241,124herbherb: 608608shrubshrub: 223223vinevine: 1616
n / missing1,971 / 0
Classes4
Balance (entropy)0.7
Imbalance ratio7e+01
Top classtree (1,124)

family

target · categorical
family classesSapindaceaeSapindaceae: 268268PoaceaePoaceae: 263263SalicaceaeSalicaceae: 198198BetulaceaeBetulaceae: 193193FagaceaeFagaceae: 122122PinaceaePinaceae: 114114AsteraceaeAsteraceae: 111111RosaceaeRosaceae: 9999TyphaceaeTyphaceae: 7373EricaceaeEricaceae: 7272+10 more+10 more: 332332
n / missing1,971 / 0
Classes31
Balance (entropy)0.84
Imbalance ratio9e+01
Top classSapindaceae (268)

genus

target · categorical
genus classesAcerAcer: 265265PopulusPopulus: 183183BetulaBetula: 159159SolidagoSolidago: 9393PhragmitesPhragmites: 7878BromusBromus: 7676QuercusQuercus: 7575PhalarisPhalaris: 7575TyphaTypha: 7373AgonisAgonis: 6868+10 more+10 more: 314314
n / missing1,971 / 0
Classes66
Balance (entropy)0.85
Imbalance ratio9e+01
Top classAcer (265)

functional.group

target · categorical
functional.group classesbroadleafbroadleaf: 996996graminoidgraminoid: 359359forbforb: 242242shrubshrub: 223223coniferconifer: 128128vinevine: 1616fernfern: 77
n / missing1,971 / 0
Classes7
Balance (entropy)0.72
Imbalance ratio1e+02
Top classbroadleaf (996)

SLA

target · numeric
SLA distribution02505007502.453 – 7.286: 2017.286 – 12.12: 26012.12 – 16.95: 66616.95 – 21.79: 41521.79 – 26.62: 22826.62 – 31.45: 8331.45 – 36.29: 4136.29 – 41.12: 1241.12 – 45.96: 945.96 – 50.79: 950.79 – 55.62: 455.62 – 60.46: 260.46 – 65.29: 465.29 – 70.12: 170.12 – 74.96: 374.96 – 79.79: 079.79 – 84.63: 084.63 – 89.46: 089.46 – 94.29: 294.29 – 99.13: 199.13 – 104: 0104 – 108.8: 1108.8 – 113.6: 0113.6 – 118.5: 1050100150
n / missing1,971 / 28
Mean ± SD17.1 ± 9.32
Median15.83
Range2.453 – 118.5
CV0.545
Skew / kurtosis3.1 / 22
Normal?no

LDMC

target · numeric
LDMC distribution010020030035.81 – 58.19: 1258.19 – 80.57: 880.57 – 102.9: 0102.9 – 125.3: 6125.3 – 147.7: 23147.7 – 170.1: 41170.1 – 192.5: 49192.5 – 214.9: 49214.9 – 237.2: 58237.2 – 259.6: 66259.6 – 282: 69282 – 304.4: 110304.4 – 326.8: 126326.8 – 349.1: 167349.1 – 371.5: 192371.5 – 393.9: 228393.9 – 416.3: 221416.3 – 438.7: 196438.7 – 461.1: 146461.1 – 483.4: 68483.4 – 505.8: 63505.8 – 528.2: 19528.2 – 550.6: 10550.6 – 573: 30200400600
n / missing1,971 / 41
Mean ± SD353.7 ± 92.4
Median370.8
Range35.81 – 573
CV0.261
Skew / kurtosis-0.75 / 0.4
Normal?no

Cmass

target · numeric
Cmass distribution010020030036.72 – 37.56: 137.56 – 38.41: 438.41 – 39.25: 239.25 – 40.09: 840.09 – 40.93: 540.93 – 41.78: 3441.78 – 42.62: 4042.62 – 43.46: 8643.46 – 44.31: 12344.31 – 45.15: 24045.15 – 45.99: 24545.99 – 46.84: 17546.84 – 47.68: 19847.68 – 48.52: 23848.52 – 49.36: 21349.36 – 50.21: 13550.21 – 51.05: 8051.05 – 51.89: 6151.89 – 52.74: 5152.74 – 53.58: 1853.58 – 54.42: 454.42 – 55.26: 055.26 – 56.11: 156.11 – 56.95: 1102050100
n / missing1,971 / 8
Mean ± SD46.92 ± 2.71
Median46.92
Range36.72 – 56.95
CV0.0577
Skew / kurtosis-0.0034 / -0.053
Normal?yes

Nmass

target · numeric
Nmass distribution01002003000.7948 – 1.01: 391.01 – 1.225: 1071.225 – 1.44: 1181.44 – 1.655: 1221.655 – 1.869: 1611.869 – 2.084: 2372.084 – 2.299: 2782.299 – 2.514: 2532.514 – 2.729: 2372.729 – 2.944: 1442.944 – 3.159: 963.159 – 3.374: 583.374 – 3.589: 373.589 – 3.804: 273.804 – 4.019: 194.019 – 4.234: 54.234 – 4.449: 84.449 – 4.663: 44.663 – 4.878: 34.878 – 5.093: 35.093 – 5.308: 15.308 – 5.523: 25.523 – 5.738: 25.738 – 5.953: 20246
n / missing1,971 / 8
Mean ± SD2.257 ± 0.709
Median2.234
Range0.7948 – 5.953
CV0.314
Skew / kurtosis0.71 / 1.8
Normal?no

solubles_mass

target · numeric
solubles_mass distribution010020030023.68 – 26.4: 126.4 – 29.12: 229.12 – 31.84: 131.84 – 34.56: 634.56 – 37.28: 3937.28 – 40: 6440 – 42.72: 7242.72 – 45.44: 6745.44 – 48.16: 7448.16 – 50.88: 5350.88 – 53.6: 5653.6 – 56.31: 6556.31 – 59.03: 9059.03 – 61.75: 9861.75 – 64.47: 12664.47 – 67.19: 14167.19 – 69.91: 18769.91 – 72.63: 21972.63 – 75.35: 20475.35 – 78.07: 16078.07 – 80.79: 11580.79 – 83.51: 4983.51 – 86.23: 2286.23 – 88.95: 8102050100
n / missing1,971 / 52
Mean ± SD63.9 ± 12.8
Median67.26
Range23.68 – 88.95
CV0.201
Skew / kurtosis-0.64 / -0.57
Normal?no

hemicellulose_mass

target · numeric
hemicellulose_mass distribution02004002.229 – 3.925: 123.925 – 5.621: 465.621 – 7.317: 2147.317 – 9.012: 3369.012 – 10.71: 28110.71 – 12.4: 23112.4 – 14.1: 14014.1 – 15.8: 9015.8 – 17.49: 6017.49 – 19.19: 6219.19 – 20.88: 3520.88 – 22.58: 5222.58 – 24.27: 4624.27 – 25.97: 7025.97 – 27.67: 5827.67 – 29.36: 4729.36 – 31.06: 3931.06 – 32.75: 4332.75 – 34.45: 3034.45 – 36.14: 1436.14 – 37.84: 137.84 – 39.54: 539.54 – 41.23: 241.23 – 42.93: 10204060
n / missing1,971 / 56
Mean ± SD14.15 ± 7.95
Median11.07
Range2.229 – 42.93
CV0.562
Skew / kurtosis1.1 / 0.2
Normal?no

cellulose_mass

target · numeric
cellulose_mass distribution01002003005.19 – 6.404: 306.404 – 7.618: 1547.618 – 8.831: 2308.831 – 10.04: 26310.04 – 11.26: 24411.26 – 12.47: 19712.47 – 13.69: 14813.69 – 14.9: 10614.9 – 16.11: 6416.11 – 17.33: 5617.33 – 18.54: 4918.54 – 19.75: 1919.75 – 20.97: 1620.97 – 22.18: 1422.18 – 23.39: 4023.39 – 24.61: 5924.61 – 25.82: 7725.82 – 27.04: 6827.04 – 28.25: 4428.25 – 29.46: 3029.46 – 30.68: 1830.68 – 31.89: 1631.89 – 33.1: 333.1 – 34.32: 1010203040
n / missing1,971 / 25
Mean ± SD13.95 ± 6.58
Median11.54
Range5.19 – 34.32
CV0.472
Skew / kurtosis1.1 / -0.021
Normal?no

lignin_mass

target · numeric
lignin_mass distribution01002000.5314 – 1.486: 691.486 – 2.44: 1952.44 – 3.395: 1153.395 – 4.349: 974.349 – 5.303: 1405.303 – 6.258: 1566.258 – 7.212: 1657.212 – 8.167: 1688.167 – 9.121: 1499.121 – 10.08: 14910.08 – 11.03: 13611.03 – 11.98: 9111.98 – 12.94: 7812.94 – 13.89: 7113.89 – 14.85: 5514.85 – 15.8: 4115.8 – 16.76: 2416.76 – 17.71: 1717.71 – 18.67: 1318.67 – 19.62: 719.62 – 20.57: 520.57 – 21.53: 221.53 – 22.48: 222.48 – 23.44: 10102030
n / missing1,971 / 25
Mean ± SD7.68 ± 4.24
Median7.372
Range0.5314 – 23.44
CV0.552
Skew / kurtosis0.45 / -0.24
Normal?no

chlA_mass

target · numeric
chlA_mass distribution02004001.242 – 2.381: 1152.381 – 3.519: 1733.519 – 4.658: 1664.658 – 5.796: 2485.796 – 6.935: 3116.935 – 8.073: 2648.073 – 9.212: 2169.212 – 10.35: 13210.35 – 11.49: 9311.49 – 12.63: 6612.63 – 13.77: 5313.77 – 14.9: 3214.9 – 16.04: 2516.04 – 17.18: 1117.18 – 18.32: 1118.32 – 19.46: 819.46 – 20.6: 220.6 – 21.74: 321.74 – 22.87: 222.87 – 24.01: 324.01 – 25.15: 225.15 – 26.29: 126.29 – 27.43: 127.43 – 28.57: 30102030
n / missing1,971 / 30
Mean ± SD7.26 ± 3.7
Median6.811
Range1.242 – 28.57
CV0.509
Skew / kurtosis1.3 / 3.3
Normal?no

chlB_mass

target · numeric
chlB_mass distribution02004000.388 – 0.8227: 1210.8227 – 1.257: 2001.257 – 1.692: 2191.692 – 2.127: 3312.127 – 2.562: 3342.562 – 2.996: 2442.996 – 3.431: 1703.431 – 3.866: 1033.866 – 4.301: 674.301 – 4.735: 554.735 – 5.17: 355.17 – 5.605: 225.605 – 6.04: 136.04 – 6.474: 46.474 – 6.909: 76.909 – 7.344: 47.344 – 7.779: 27.779 – 8.213: 28.213 – 8.648: 38.648 – 9.083: 19.083 – 9.518: 09.518 – 9.952: 09.952 – 10.39: 210.39 – 10.82: 2051015
n / missing1,971 / 30
Mean ± SD2.434 ± 1.27
Median2.259
Range0.388 – 10.82
CV0.522
Skew / kurtosis1.5 / 4.9
Normal?no

car_mass

target · numeric
car_mass distribution02004000.193 – 0.4057: 460.4057 – 0.6183: 1300.6183 – 0.831: 940.831 – 1.044: 1431.044 – 1.256: 2491.256 – 1.469: 3111.469 – 1.682: 2731.682 – 1.894: 2021.894 – 2.107: 1452.107 – 2.32: 952.32 – 2.532: 782.532 – 2.745: 582.745 – 2.957: 362.957 – 3.17: 203.17 – 3.383: 183.383 – 3.595: 193.595 – 3.808: 63.808 – 4.021: 54.021 – 4.233: 44.233 – 4.446: 14.446 – 4.659: 24.659 – 4.871: 24.871 – 5.084: 25.084 – 5.297: 20246
n / missing1,971 / 30
Mean ± SD1.554 ± 0.715
Median1.464
Range0.193 – 5.297
CV0.46
Skew / kurtosis0.99 / 2.1
Normal?no

Al_mass

target · numeric
Al_mass distribution01002000 – 0.01383: 730.01383 – 0.02767: 1260.02767 – 0.0415: 1530.0415 – 0.05533: 1090.05533 – 0.06917: 650.06917 – 0.083: 650.083 – 0.09683: 260.09683 – 0.1107: 240.1107 – 0.1245: 80.1245 – 0.1383: 30.1383 – 0.1522: 30.1522 – 0.166: 60.166 – 0.1798: 30.1798 – 0.1937: 60.1937 – 0.2075: 30.2075 – 0.2213: 10.2213 – 0.2352: 10.2352 – 0.249: 00.249 – 0.2628: 00.2628 – 0.2767: 00.2767 – 0.2905: 00.2905 – 0.3043: 10.3043 – 0.3182: 00.3182 – 0.332: 10.00.10.20.30.4
n / missing1,971 / 1,294
Mean ± SD0.04917 ± 0.039
Median0.04
Range0 – 0.332
CV0.794
Skew / kurtosis2.3 / 8.9
Normal?no

B208.9_mass

target · numeric
B208.9_mass distribution050100-0.29 – -0.26: 1-0.26 – -0.23: 19-0.23 – -0.2: 9-0.2 – -0.17: 17-0.17 – -0.14: 19-0.14 – -0.11: 8-0.11 – -0.08: 17-0.08 – -0.05: 24-0.05 – -0.02: 30-0.02 – 0.01: 720.01 – 0.04: 690.04 – 0.07: 910.07 – 0.1: 720.1 – 0.13: 510.13 – 0.16: 290.16 – 0.19: 150.19 – 0.22: 170.22 – 0.25: 130.25 – 0.28: 50.28 – 0.31: 90.31 – 0.34: 130.34 – 0.37: 40.37 – 0.4: 40.4 – 0.43: 2-0.4-0.20.00.20.40.6
n / missing1,971 / 1,361
Mean ± SD0.04166 ± 0.127
Median0.045
Range-0.29 – 0.43
CV3.04
Skew / kurtosis0.02 / 0.57
Normal?yes

B249.8_mass

target · numeric
B249.8_mass distribution050100-0.26 – -0.2325: 3-0.2325 – -0.205: 18-0.205 – -0.1775: 12-0.1775 – -0.15: 16-0.15 – -0.1225: 16-0.1225 – -0.095: 8-0.095 – -0.0675: 19-0.0675 – -0.04: 22-0.04 – -0.0125: 34-0.0125 – 0.015: 720.015 – 0.0425: 750.0425 – 0.07: 880.07 – 0.0975: 610.0975 – 0.125: 610.125 – 0.1525: 240.1525 – 0.18: 140.18 – 0.2075: 170.2075 – 0.235: 140.235 – 0.2625: 60.2625 – 0.29: 130.29 – 0.3175: 70.3175 – 0.345: 60.345 – 0.3725: 20.3725 – 0.4: 2-0.4-0.20.00.20.4
n / missing1,971 / 1,361
Mean ± SD0.04184 ± 0.117
Median0.045
Range-0.26 – 0.4
CV2.8
Skew / kurtosis-0.0061 / 0.53
Normal?yes

Ca_mass

target · numeric
Ca_mass distribution0501000.959 – 2.441: 232.441 – 3.924: 523.924 – 5.406: 765.406 – 6.889: 816.889 – 8.371: 948.371 – 9.854: 659.854 – 11.34: 7711.34 – 12.82: 4312.82 – 14.3: 3814.3 – 15.78: 2915.78 – 17.27: 2217.27 – 18.75: 1718.75 – 20.23: 1820.23 – 21.71: 1321.71 – 23.2: 523.2 – 24.68: 624.68 – 26.16: 526.16 – 27.64: 027.64 – 29.13: 229.13 – 30.61: 430.61 – 32.09: 432.09 – 33.57: 333.57 – 35.06: 035.06 – 36.54: 1010203040
n / missing1,971 / 1,293
Mean ± SD9.988 ± 5.97
Median8.58
Range0.959 – 36.54
CV0.598
Skew / kurtosis1.3 / 2.2
Normal?no

Cu_mass

target · numeric
Cu_mass distribution01002003000 – 0.001125: 500.001125 – 0.00225: 420.00225 – 0.003375: 350.003375 – 0.0045: 700.0045 – 0.005625: 700.005625 – 0.00675: 710.00675 – 0.007875: 530.007875 – 0.009: 490.009 – 0.01012: 2060.01012 – 0.01125: 130.01125 – 0.01237: 40.01237 – 0.0135: 10.0135 – 0.01462: 20.01462 – 0.01575: 00.01575 – 0.01687: 10.01687 – 0.018: 10.018 – 0.01912: 10.01912 – 0.02025: 10.02025 – 0.02137: 20.02137 – 0.0225: 10.0225 – 0.02362: 00.02362 – 0.02475: 10.02475 – 0.02587: 00.02587 – 0.027: 10.000.010.020.03
n / missing1,971 / 1,296
Mean ± SD0.006616 ± 0.00362
Median0.006
Range0 – 0.027
CV0.547
Skew / kurtosis0.6 / 2.5
Normal?no

Fe_mass

target · numeric
Fe_mass distribution0501000.01497 – 0.02455: 340.02455 – 0.03414: 760.03414 – 0.04372: 670.04372 – 0.05331: 900.05331 – 0.06289: 980.06289 – 0.07248: 920.07248 – 0.08206: 620.08206 – 0.09165: 460.09165 – 0.1012: 330.1012 – 0.1108: 130.1108 – 0.1204: 190.1204 – 0.13: 30.13 – 0.1396: 120.1396 – 0.1492: 70.1492 – 0.1587: 110.1587 – 0.1683: 30.1683 – 0.1779: 10.1779 – 0.1875: 10.1875 – 0.1971: 20.1971 – 0.2067: 20.2067 – 0.2162: 20.2162 – 0.2258: 20.2258 – 0.2354: 00.2354 – 0.245: 10.00.10.20.3
n / missing1,971 / 1,294
Mean ± SD0.06682 ± 0.0348
Median0.06
Range0.01497 – 0.245
CV0.52
Skew / kurtosis1.5 / 3.5
Normal?no

K_mass

target · numeric
K_mass distribution0501001501.058 – 2.141: 202.141 – 3.224: 633.224 – 4.307: 844.307 – 5.389: 1365.389 – 6.472: 1256.472 – 7.555: 1007.555 – 8.638: 578.638 – 9.72: 319.72 – 10.8: 2910.8 – 11.89: 711.89 – 12.97: 312.97 – 14.05: 314.05 – 15.13: 315.13 – 16.22: 316.22 – 17.3: 317.3 – 18.38: 418.38 – 19.47: 319.47 – 20.55: 220.55 – 21.63: 021.63 – 22.71: 022.71 – 23.8: 123.8 – 24.88: 024.88 – 25.96: 025.96 – 27.05: 10102030
n / missing1,971 / 1,293
Mean ± SD6.155 ± 3.06
Median5.737
Range1.058 – 27.05
CV0.497
Skew / kurtosis2.1 / 7.6
Normal?no

Mg_mass

target · numeric
Mg_mass distribution0501000.482 – 0.7895: 380.7895 – 1.097: 621.097 – 1.405: 351.405 – 1.712: 631.712 – 2.019: 952.019 – 2.327: 972.327 – 2.635: 982.635 – 2.942: 682.942 – 3.25: 503.25 – 3.557: 263.557 – 3.864: 223.864 – 4.172: 84.172 – 4.479: 34.479 – 4.787: 34.787 – 5.095: 55.095 – 5.402: 05.402 – 5.71: 15.71 – 6.017: 06.017 – 6.325: 16.325 – 6.632: 06.632 – 6.939: 16.939 – 7.247: 07.247 – 7.554: 07.554 – 7.862: 202468
n / missing1,971 / 1,293
Mean ± SD2.193 ± 0.95
Median2.143
Range0.482 – 7.862
CV0.433
Skew / kurtosis1.1 / 4.1
Normal?no

Mn_mass

target · numeric
Mn_mass distribution01002000 – 0.05825: 1900.05825 – 0.1165: 1570.1165 – 0.1747: 770.1747 – 0.233: 490.233 – 0.2913: 370.2913 – 0.3495: 370.3495 – 0.4077: 250.4077 – 0.466: 260.466 – 0.5242: 200.5242 – 0.5825: 140.5825 – 0.6407: 100.6407 – 0.699: 80.699 – 0.7572: 60.7572 – 0.8155: 50.8155 – 0.8737: 40.8737 – 0.932: 40.932 – 0.9902: 30.9902 – 1.048: 11.048 – 1.107: 31.107 – 1.165: 01.165 – 1.223: 01.223 – 1.281: 11.281 – 1.34: 01.34 – 1.398: 10.00.51.01.5
n / missing1,971 / 1,293
Mean ± SD0.2016 ± 0.217
Median0.11
Range0 – 1.398
CV1.08
Skew / kurtosis1.9 / 4.1
Normal?no

Na_mass

target · numeric
Na_mass distribution0501001500 – 0.203: 930.203 – 0.406: 650.406 – 0.609: 1040.609 – 0.812: 1430.812 – 1.015: 1211.015 – 1.218: 611.218 – 1.421: 241.421 – 1.624: 121.624 – 1.827: 141.827 – 2.03: 102.03 – 2.233: 22.233 – 2.436: 102.436 – 2.639: 32.639 – 2.842: 52.842 – 3.045: 33.045 – 3.248: 23.248 – 3.451: 03.451 – 3.654: 03.654 – 3.857: 33.857 – 4.06: 14.06 – 4.263: 14.263 – 4.466: 04.466 – 4.669: 04.669 – 4.872: 10246
n / missing1,971 / 1,293
Mean ± SD0.7995 ± 0.625
Median0.72
Range0 – 4.872
CV0.782
Skew / kurtosis2.1 / 7.5
Normal?no

P_mass

target · numeric
P_mass distribution01002000.2228 – 0.5126: 210.5126 – 0.8024: 530.8024 – 1.092: 921.092 – 1.382: 1991.382 – 1.672: 1461.672 – 1.962: 631.962 – 2.251: 242.251 – 2.541: 182.541 – 2.831: 102.831 – 3.121: 103.121 – 3.411: 63.411 – 3.7: 93.7 – 3.99: 53.99 – 4.28: 64.28 – 4.57: 14.57 – 4.86: 34.86 – 5.149: 25.149 – 5.439: 15.439 – 5.729: 35.729 – 6.019: 26.019 – 6.309: 16.309 – 6.598: 16.598 – 6.888: 06.888 – 7.178: 202468
n / missing1,971 / 1,293
Mean ± SD1.553 ± 0.914
Median1.354
Range0.2228 – 7.178
CV0.589
Skew / kurtosis2.7 / 9.9
Normal?no

Zn_mass

target · numeric
Zn_mass distribution01002003000.001424 – 0.01678: 2260.01678 – 0.03214: 1510.03214 – 0.0475: 250.0475 – 0.06285: 150.06285 – 0.07821: 280.07821 – 0.09357: 300.09357 – 0.1089: 320.1089 – 0.1243: 470.1243 – 0.1396: 180.1396 – 0.155: 260.155 – 0.1704: 170.1704 – 0.1857: 140.1857 – 0.2011: 130.2011 – 0.2164: 60.2164 – 0.2318: 70.2318 – 0.2471: 60.2471 – 0.2625: 30.2625 – 0.2779: 20.2779 – 0.2932: 60.2932 – 0.3086: 10.3086 – 0.3239: 10.3239 – 0.3393: 10.3393 – 0.3546: 10.3546 – 0.37: 10.00.10.20.30.4
n / missing1,971 / 1,294
Mean ± SD0.06444 ± 0.07
Median0.029
Range0.001424 – 0.37
CV1.09
Skew / kurtosis1.4 / 1.6
Normal?no

LMA

target · numeric
LMA distribution02004006000.008442 – 0.02508: 380.02508 – 0.04171: 2420.04171 – 0.05835: 5200.05835 – 0.07499: 5400.07499 – 0.09162: 2430.09162 – 0.1083: 700.1083 – 0.1249: 620.1249 – 0.1415: 350.1415 – 0.1582: 360.1582 – 0.1748: 290.1748 – 0.1914: 320.1914 – 0.2081: 210.2081 – 0.2247: 140.2247 – 0.2414: 150.2414 – 0.258: 60.258 – 0.2746: 50.2746 – 0.2913: 30.2913 – 0.3079: 80.3079 – 0.3245: 90.3245 – 0.3412: 30.3412 – 0.3578: 30.3578 – 0.3744: 20.3744 – 0.3911: 60.3911 – 0.4077: 10.00.10.20.30.40.5
n / missing1,971 / 28
Mean ± SD0.07772 ± 0.0539
Median0.06318
Range0.008442 – 0.4077
CV0.694
Skew / kurtosis2.8 / 9.2
Normal?no

EWT

target · numeric
EWT distribution02505007500.03745 – 0.06377: 550.06377 – 0.09008: 3880.09008 – 0.1164: 6300.1164 – 0.1427: 2660.1427 – 0.169: 2090.169 – 0.1953: 900.1953 – 0.2217: 370.2217 – 0.248: 370.248 – 0.2743: 380.2743 – 0.3006: 210.3006 – 0.3269: 190.3269 – 0.3532: 170.3532 – 0.3796: 180.3796 – 0.4059: 100.4059 – 0.4322: 210.4322 – 0.4585: 260.4585 – 0.4848: 120.4848 – 0.5111: 160.5111 – 0.5375: 50.5375 – 0.5638: 100.5638 – 0.5901: 10.5901 – 0.6164: 20.6164 – 0.6427: 00.6427 – 0.669: 10.00.20.40.60.8
n / missing1,971 / 42
Mean ± SD0.1456 ± 0.0961
Median0.1113
Range0.03745 – 0.669
CV0.66
Skew / kurtosis2.4 / 5.5
Normal?no

Metadata 4

site

metadata · categorical
site classesIdB_ile_ste_margIdB_ile_ste_marg: 260260GroboisFieldELGroboisFieldEL: 245245MtMeg-1MtMeg-1: 204204CGOP_1CGOP_1: 201201CF_MSBCF_MSB: 175175MBP_MSc_RBRMBP_MSc_RBR: 6969Frayere_maraisFrayere_marais: 6363Oka_PlageOka_Plage: 6161SBLUdeMSBLUdeM: 6060SWA-WarrenSWA-Warren: 5858+10 more+10 more: 328328
n / missing1,971 / 0
Classes41
Balance (entropy)0.82
Imbalance ratio260
Top classIdB_ile_ste_marg (260)

latitude

metadata · numeric
latitude distribution01,0002,000-34.81 – -31.32: 68-31.32 – -27.84: 0-27.84 – -24.36: 0-24.36 – -20.87: 0-20.87 – -17.39: 0-17.39 – -13.9: 0-13.9 – -10.42: 0-10.42 – -6.936: 0-6.936 – -3.452: 0-3.452 – 0.03169: 00.03169 – 3.516: 03.516 – 7: 07 – 10.48: 010.48 – 13.97: 013.97 – 17.45: 017.45 – 20.94: 020.94 – 24.42: 024.42 – 27.9: 027.9 – 31.39: 031.39 – 34.87: 034.87 – 38.36: 038.36 – 41.84: 041.84 – 45.32: 2045.32 – 48.81: 1883-50-2502550
n / missing1,971 / 0
Mean ± SD43.15 ± 14.7
Median45.6
Range-34.81 – 48.81
CV0.342
Skew / kurtosis-5.1 / 24
Normal?no

longitude

metadata · numeric
longitude distribution05001,0001,500-123.6 – -113.6: 201-113.6 – -103.7: 0-103.7 – -93.67: 0-93.67 – -83.68: 0-83.68 – -73.69: 412-73.69 – -63.71: 1290-63.71 – -53.72: 0-53.72 – -43.73: 0-43.73 – -33.74: 0-33.74 – -23.76: 0-23.76 – -13.77: 0-13.77 – -3.781: 0-3.781 – 6.207: 06.207 – 16.19: 016.19 – 26.18: 026.18 – 36.17: 036.17 – 46.16: 046.16 – 56.14: 056.14 – 66.13: 066.13 – 76.12: 076.12 – 86.11: 086.11 – 96.09: 096.09 – 106.1: 0106.1 – 116.1: 68-200-1000100200
n / missing1,971 / 0
Mean ± SD-72.01 ± 38.6
Median-73.47
Range-123.6 – 116.1
CV0.537
Skew / kurtosis3.7 / 17
Normal?no

date

metadata · categorical
date classes2019-07-252019-07-25: 37372018-07-242018-07-24: 31312019-07-242019-07-24: 31312019-07-092019-07-09: 30302019-07-102019-07-10: 30302019-05-122019-05-12: 30302019-07-182019-07-18: 29292019-05-112019-05-11: 29292018-07-252018-07-25: 28282019-08-132019-08-13: 2828+10 more+10 more: 255255
n / missing1,971 / 0
Classes130
Balance (entropy)0.97
Imbalance ratio37
Top class2019-07-25 (37)
Constant metadata 18
  • ecosis_resource_id45eb2727-d77c-454c-bf15-fcbe822dc82a
  • coordinate_precision_notessource-provided coordinates when available
  • year2,022
  • plant_partLeaf
  • canopy_or_leafleaf
  • instrumentSpectra Vista Corporation HR-1024i
  • acquisition_modeContact
  • signal_typeabsorbance
  • axis_unitnm
  • axis_min400
  • axis_max2,400
  • n_points_original2,001
  • publication_doi10.1080/00103624.2016.1228952 | 10.1101/2022.07.01.498461 | 10.1111/1365-2745.13972 | 10.21232/44vxHorW | 10.21232/deP7jVyq | 10.21232/dep7jvyq
  • citationShan Kothari, Rosalie Beauchamp-Rioux, Florence Blanchard, Anna L. Crofts, Alize Girard, Xavier Guilbeault-Mayers, Paul W. Hacker, Juliana Pardo, Anna K. Schweiger, Sabrina Demers-Thibeault, Anne Bruneau, Nicholas C. Coops, Margaret Kalacska, Mark Vellend and Etienne Lalibert. 2022. CABO 2018-2019 Leaf-Level Spectra. Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS). 10.21232/44vxHorW
  • licenseCreative Commons Attribution
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • notesEcoSIS package cabo-2018-2019-leaf-level-spectra, no interpolation applied by project.

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

Alignment

Alignment levelobservation
Sample id availableyes
Samples1,971
Observations (total)1,971
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorCABO 2018-2019 Leaf-Level Spectra
Origin · url [open]https://data.ecosis.org/dataset/cabo-2018-2019-leaf-level-spectra
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.1111/1365-2745.13972 — Plant spectra as integrative measures of phenotypes
Publication10.1101/2022.07.01.498461 — Predicting leaf traits across functional groups using reflectance spectroscopy
Publication10.21232/44vxHorW — CABO 2018-2019 Leaf-Level Spectra
Publication10.1080/00103624.2016.1228952
Publication10.21232/deP7jVyq
Publication10.21232/dep7jvyq

Governance & integrity

Tierpublic
LicenseCC-BY-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionEcoSIS CKAN metadata exposes an open license.
Content version1.0.0
Schema / protocol2.0
Content hashfd0f85db1d8e5656…
Processing hash3f25ffe02c3d9779…
Metadata hash6b036506357ce0e2…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("ecosis_cabo_2018_2019_leaf_level_spectra_absorbance_nirs")            # DOI-pinned, checksum-verified, cached
X, y = ds.x(), ds.y()
print(X.shape, y.shape)
card.jsoncroissant.jsonIdentity metadata only — the dataset bytes live at the origin / DOI.