{"id":"W3009445648","doi":"10.3390/rs12050824","title":"Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agricultural Research Service; Colorado State University; U.S. Department of Agriculture","keywords":"Normalized Difference Vegetation Index; Anthesis; Agronomy; Growing season; Yield (engineering); Environmental science; Crop; Mathematics; Leaf area index; Biology; Cultivar","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":[0.00008308133,0.0002146438,0.0002229851,0.00002718693,0.00029986,0.00009599255,0.00007069197,0.00008798003,0.00004735396],"category_scores_gemma":[0.00007741148,0.0001793577,0.00004228586,0.0003808157,0.0001381702,0.0001423813,0.0001938303,0.0001949234,0.0001268786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307193,"about_ca_system_score_gemma":0.000009126972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004591515,"about_ca_topic_score_gemma":0.0001832241,"domain_scores_codex":[0.9985722,0.00009870225,0.0001775516,0.0005609944,0.0002583751,0.0003321747],"domain_scores_gemma":[0.9993919,0.00003216662,0.00006475121,0.0002082247,0.00001993351,0.0002830503],"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":[0.00001456517,0.000008122438,0.0002703376,0.00001198674,0.00002202045,0.00002003642,0.0008184164,0.01060758,0.9781216,0.000004579867,0.000439402,0.0096614],"study_design_scores_gemma":[0.0008241383,0.0001416746,0.1198902,0.0002284448,0.0002432812,0.0004953315,0.0003849752,0.7305013,0.1409909,0.00236783,0.002602052,0.001329866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802364,0.00002622366,0.0144469,0.003415541,0.0001385689,0.000302545,0.000004646215,0.0001214723,0.001307672],"genre_scores_gemma":[0.9737501,0.000004326233,0.0250522,0.0008858394,0.0001538921,3.686006e-9,0.000008833415,0.0000264932,0.0001183644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8371307,"threshold_uncertainty_score":0.7313995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313711688264568,"score_gpt":0.2521443888656571,"score_spread":0.2207732200392003,"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."}}