{"id":"W2958199191","doi":"10.3390/rs11141684","title":"Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Key Research and Development Program of China","keywords":"Evapotranspiration; Irrigation; Environmental science; Canopy; Leaf area index; Water content; Crop coefficient; Biomass (ecology); Remote sensing; Agronomy; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007247941,0.0005495246,0.0005443164,0.00009233686,0.000346619,0.0002712938,0.0005099292,0.0001602412,0.00005904113],"category_scores_gemma":[0.00004674584,0.000360437,0.00007924387,0.0003835602,0.0001173962,0.0004315932,0.001059738,0.0005310934,0.001088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004272373,"about_ca_system_score_gemma":0.00002083976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001102562,"about_ca_topic_score_gemma":0.0007895971,"domain_scores_codex":[0.9958881,0.0001013563,0.0004679838,0.00158057,0.0009275544,0.001034484],"domain_scores_gemma":[0.99744,0.00005605988,0.0001108279,0.002029183,0.00005739675,0.0003065642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000161122,0.00004156243,0.0002636369,0.00001580995,0.00009299532,0.0002678383,0.005285892,0.4707039,0.4873129,2.41059e-7,0.000591952,0.03526211],"study_design_scores_gemma":[0.0007766591,0.0001832494,0.0004696041,0.0002067841,0.0001049086,0.0006017826,0.0009910469,0.9807927,0.01478826,0.00006455382,0.0003431273,0.0006773443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176064,0.00001087055,0.07611407,0.0007925223,0.0002476435,0.001047011,0.000003104197,0.000192526,0.003985831],"genre_scores_gemma":[0.7567877,0.000002978862,0.2412588,0.0004354268,0.0001358062,1.508078e-9,0.00001791582,0.00008672442,0.001274666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5100887,"threshold_uncertainty_score":0.9998848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02013209996243171,"score_gpt":0.2458981508398685,"score_spread":0.2257660508774368,"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."}}