{"id":"W1772733177","doi":"10.5589/m08-017","title":"Soil salt content estimation in the Yellow River delta with satellite hyperspectral data","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nanjing University; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Partial least squares regression; Mean squared error; Soil salinity; Soil water; Environmental science; Coefficient of determination; Multivariate statistics; Soil science; Linear regression; Soil test; Sampling (signal processing); Mathematics; Calibration; Remote sensing; Statistics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005033379,0.0004010929,0.0002541777,0.0004772252,0.0001115587,0.0003551326,0.000225672,0.0002389141,0.000204033],"category_scores_gemma":[0.0006108938,0.0001793263,0.0002292806,0.0004567073,0.0001205999,0.0004621452,0.0002865925,0.0001578912,0.0001187565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147175,"about_ca_system_score_gemma":0.0002694866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165343,"about_ca_topic_score_gemma":0.01778792,"domain_scores_codex":[0.9998564,0.00003104721,0.000009635327,0.00004933746,0.00003960257,0.00001401218],"domain_scores_gemma":[0.9998204,0.0000500405,0.00003654766,0.00001808828,0.00006137689,0.00001349159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000350021,0.0002096241,0.6240797,0.0001333468,0.0001436762,0.0003609473,0.0003459774,0.07043117,0.1756481,0.0001021048,0.0003627746,0.1278326],"study_design_scores_gemma":[0.00002474839,0.00009366221,0.4962404,0.00001113477,0.00006568451,0.00007066988,0.0002522515,0.4773089,0.02532689,0.0001136346,0.0004628096,0.00002918944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965776,0.00003869554,0.003064978,0.0000128043,0.000001008158,0.000006136869,0.00008618704,0.00005224748,0.0001604292],"genre_scores_gemma":[0.9929299,0.00006851159,0.006373136,0.000007797605,0.000001819069,0.000008324114,0.0002918917,0.000008180069,0.0003105995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01165343,"threshold_uncertainty_score":0.02317119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04463194553889378,"score_gpt":0.2249818618719097,"score_spread":0.180349916333016,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}