{"id":"W1634020409","doi":"10.1029/2009gb003765","title":"Simulating the effects of climate and agricultural management practices on global crop yield","year":2011,"lang":"en","type":"article","venue":"Global Biogeochemical Cycles","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":384,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Crop yield; Sowing; Agriculture; Yield (engineering); Irrigation; Crop simulation model; Crop; Agronomy; DSSAT; Climate change; Precipitation; Agricultural engineering; Geography; Meteorology; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001203651,0.0002471991,0.0002212075,0.000003682277,0.0001762005,0.00006730176,0.0003302072,0.0001462664,0.00006738245],"category_scores_gemma":[0.0003367557,0.00007146005,0.0001114149,0.0003905839,0.0001429413,0.0001209788,0.0002923066,0.0001106398,0.0000191796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004912403,"about_ca_system_score_gemma":0.00000146057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004826693,"about_ca_topic_score_gemma":0.0002411624,"domain_scores_codex":[0.9986127,0.00005946186,0.0002489332,0.0003605597,0.0002890413,0.0004292785],"domain_scores_gemma":[0.9989987,0.000366915,0.000343697,0.00009095951,0.00005776952,0.0001419544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006606843,0.001205032,0.3365032,0.0005611913,0.0003156904,0.0000875178,0.0004535877,0.000001945017,0.4677543,0.008533866,0.004503492,0.1794195],"study_design_scores_gemma":[0.000233802,0.0003896684,0.964727,0.0002319041,0.0001046875,0.0000341144,0.001347898,0.000006192605,0.03029174,0.001593121,0.0007430387,0.0002967844],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860106,0.0004626686,1.068908e-7,0.000812636,0.0001067787,0.0003666921,0.0003519585,0.00007688653,0.0118117],"genre_scores_gemma":[0.9989178,0.0003999935,0.0001437956,0.0003460402,0.000116311,0.00001268077,0.00005818655,7.005105e-7,0.000004524819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6282238,"threshold_uncertainty_score":0.2914056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536026313114021,"score_gpt":0.2652247790480992,"score_spread":0.229864515916959,"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."}}