{"id":"W4386617341","doi":"10.1088/1748-9326/acf871","title":"Likely impacts of the 2022 heatwave on India’s wheat production","year":2023,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Yield (engineering); Crop; Production (economics); Crop production; Agronomy; Environmental science; Growing season; Winter wheat; Climate change; Crop yield; Geography; Agricultural economics; Agriculture; Biology; Ecology; Economics","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":[0.0006808079,0.0001207418,0.0001070728,0.0000361923,0.0003019889,0.00003654094,0.0003420125,0.00006438415,0.0005046303],"category_scores_gemma":[0.0001619628,0.00003724146,0.00009118245,0.000754427,0.0002414196,0.000105188,0.0002534008,0.0003782271,0.0003871167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120342,"about_ca_system_score_gemma":0.000003472062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009611756,"about_ca_topic_score_gemma":0.0000908133,"domain_scores_codex":[0.9977577,0.0002579429,0.0001394018,0.0003031629,0.001023703,0.0005181261],"domain_scores_gemma":[0.9994791,0.0002112815,0.00004968953,0.00013047,0.000007169404,0.0001222965],"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":[0.00002783772,0.00007736777,0.01701985,0.00000561304,0.000007000908,0.000005338909,0.0002239603,0.000007045943,0.9261678,0.00000254387,0.05177841,0.004677159],"study_design_scores_gemma":[0.00006730109,0.0001981093,0.8518397,0.00003611899,0.000002087182,0.000004475421,0.0008259041,0.000002361033,0.1425863,0.00002495264,0.004330764,0.00008189623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574813,0.00005086497,6.846856e-9,0.04155299,0.0001658859,0.0004606366,0.000104611,0.00003209133,0.0001516093],"genre_scores_gemma":[0.997932,0.0002686945,0.000001337481,0.0007214244,0.0002850627,0.00002904042,0.00009229373,0.000001979296,0.0006681342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8348199,"threshold_uncertainty_score":0.5525348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05937152077701843,"score_gpt":0.2895118595814284,"score_spread":0.23014033880441,"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."}}