{"id":"W2948236978","doi":"10.3390/rs11111298","title":"Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph","funders":"Agriculture and Agri-Food Canada","keywords":"Hyperspectral imaging; VNIR; Remote sensing; Environmental science; Spectral bands; Partial least squares regression; Soil water; Soil carbon; Soil science; Computer science; Geology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003002016,0.0004377303,0.0003696835,0.001056724,0.001194041,0.0006793694,0.0005842887,0.0002247048,0.0006508264],"category_scores_gemma":[0.0004520536,0.0001723313,0.0003252733,0.001846854,0.0003297645,0.0001931143,0.0003434543,0.0001968662,0.0001421387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009523969,"about_ca_system_score_gemma":0.007821774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9850154,"about_ca_topic_score_gemma":0.9937136,"domain_scores_codex":[0.9997419,0.000009734634,0.000009077919,0.00007629745,0.00009196026,0.00007104797],"domain_scores_gemma":[0.9995245,0.00002100731,0.00003100424,0.00001711387,0.0003625819,0.00004380228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005071769,0.0001487002,0.8491628,0.0001637541,0.0003304007,0.0004910268,0.00139066,0.02073987,0.03403694,0.0002002562,0.003558935,0.08926944],"study_design_scores_gemma":[0.00001358902,0.0000196858,0.9683936,0.00001375582,0.00008314686,0.00003390655,0.001038605,0.02661967,0.001901545,0.00004432005,0.001816478,0.00002170721],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960757,0.0001968081,0.0008185371,0.00004861378,0.000005935183,0.0000255927,0.001323897,0.00004776074,0.001457109],"genre_scores_gemma":[0.9944775,0.0001609133,0.001448647,0.00001994238,0.000003275626,0.00001219508,0.002013131,0.00001088833,0.001853539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01498461,"threshold_uncertainty_score":0.06910157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006339464526996346,"score_gpt":0.1945695026156755,"score_spread":0.1882300380886791,"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."}}