{"id":"W3091469287","doi":"10.3390/rs12193182","title":"Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Sino-Danish Center; Joint Programming Initiative Water challenges for a changing world; Innovationsfonden","keywords":"Hyperspectral imaging; Transpiration; Canopy; Stomatal conductance; Environmental science; Photochemical Reflectance Index; Evapotranspiration; Agronomy; Photosynthesis; VNIR; Leaf area index; Normalized Difference Vegetation Index; Remote sensing; Botany; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001237594,0.0001909408,0.0002645061,0.00001892426,0.0001676727,0.00005110257,0.00003683073,0.00009807129,0.000006884623],"category_scores_gemma":[0.00005401845,0.0001628774,0.00004138467,0.0001305714,0.000338359,0.0001653824,0.00004092381,0.0001100198,0.00000154806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003412718,"about_ca_system_score_gemma":0.000007659956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001070188,"about_ca_topic_score_gemma":0.00006800326,"domain_scores_codex":[0.9989088,0.0000538626,0.0002166361,0.0004100837,0.000171789,0.0002388963],"domain_scores_gemma":[0.9995195,0.0001238365,0.00009768722,0.0001119784,0.00001781227,0.000129139],"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":[0.00004026913,0.000003575197,0.00004853846,0.00004288744,0.00002059889,0.000008282192,0.00210968,0.0001333607,0.9577102,0.0000216141,0.00004648943,0.03981452],"study_design_scores_gemma":[0.001503374,0.000202806,0.008824807,0.0001844793,0.0001907053,0.0005467082,0.002703587,0.5405261,0.4430257,0.0009769428,0.0005945185,0.0007202379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895115,0.0001221681,0.00747199,0.00163833,0.00003821355,0.0002884984,0.000007468122,0.00004486773,0.0008769744],"genre_scores_gemma":[0.9102959,0.00002525097,0.08913001,0.0004258124,0.00007166839,3.618405e-9,0.000002904803,0.00002294553,0.00002547396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5403928,"threshold_uncertainty_score":0.6641946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165136150669872,"score_gpt":0.2115263880407101,"score_spread":0.1950127729737229,"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."}}