{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003445911,0.0003278746,0.0002213807,0.0005591961,0.0002153527,0.0005613005,0.0001832863,0.0002087212,0.0003220951],"category_scores_gemma":[0.0005079625,0.0001713196,0.0001479808,0.0005416478,0.0001099283,0.0003048826,0.0001697736,0.0001740599,0.0001465293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003398596,"about_ca_system_score_gemma":0.0001369632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008096598,"about_ca_topic_score_gemma":0.03200213,"domain_scores_codex":[0.9998499,0.00002488953,0.00001580416,0.00006286961,0.00003394383,0.00001249867],"domain_scores_gemma":[0.9997632,0.00004006138,0.0000782811,0.00002040811,0.00008109479,0.00001694673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000244594,0.0001067964,0.8072944,0.00008097107,0.000134682,0.00007535366,0.0003588878,0.001283549,0.1298875,0.0000992773,0.0003525735,0.06008147],"study_design_scores_gemma":[0.000006068563,0.00007633182,0.9808776,0.00001362874,0.00003855775,0.00007939025,0.000200071,0.005482793,0.01232979,0.00008235691,0.0008026827,0.00001060016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883142,0.0002762061,0.008889161,0.00002524474,0.00000838259,0.00002424061,0.0005228819,0.00006589595,0.00187387],"genre_scores_gemma":[0.9811772,0.0001815858,0.0168737,0.0000330178,0.000004316957,0.00002026079,0.0008863151,0.00001741416,0.0008060677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008096598,"threshold_uncertainty_score":0.01609898,"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."}}