{"id":"W2054010557","doi":"10.2134/agronj2011.0199","title":"Water and Nitrogen Effects on Active Canopy Sensor Vegetation Indices","year":2011,"lang":"en","type":"article","venue":"Agronomy Journal","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Red edge; Canopy; Normalized Difference Vegetation Index; Irrigation; Environmental science; Evapotranspiration; Growing season; Vegetation (pathology); Agronomy; Crop; Enhanced vegetation index; Leaf area index; Botany; Vegetation Index; Biology; Ecology","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":[0.0007029604,0.0003367536,0.0002206831,0.0002306237,0.0001399954,0.0003038647,0.0002279333,0.000225992,0.0003175515],"category_scores_gemma":[0.001494096,0.0001528278,0.0001798158,0.000240461,0.0001662143,0.0003805132,0.0002021448,0.0001745525,0.00009435332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806991,"about_ca_system_score_gemma":0.00008889017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002575781,"about_ca_topic_score_gemma":0.003773266,"domain_scores_codex":[0.9996669,0.00008215102,0.00001706761,0.0000916335,0.0001119305,0.00003040965],"domain_scores_gemma":[0.9992265,0.0003887012,0.000118803,0.00005518399,0.0001708739,0.00003993897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00215246,0.0003040196,0.1633827,0.0001341431,0.0001589322,0.000100504,0.0002939716,0.007213164,0.7645634,0.0001213637,0.0002067079,0.06136862],"study_design_scores_gemma":[0.00003352757,0.001230545,0.749542,0.00001267197,0.0001295157,0.0001861123,0.0001501892,0.05126804,0.1963338,0.0001587102,0.00090839,0.00004654699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977921,0.00008227016,0.001668806,0.000005162413,0.000005419302,0.000006792721,0.00006516086,0.00002266351,0.0003515424],"genre_scores_gemma":[0.9971533,0.00006139762,0.002235172,0.00001300207,0.000002461835,0.000009709402,0.0002135936,0.00001212472,0.0002992511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002575781,"threshold_uncertainty_score":0.005121529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00732907799834851,"score_gpt":0.1862855209915893,"score_spread":0.1789564429932408,"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."}}