{"id":"W2227280755","doi":"10.1038/nature16467","title":"Influence of extreme weather disasters on global crop production","year":2016,"lang":"en","type":"article","venue":"Nature","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":3730,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Extreme weather; Environmental science; Extreme heat; Agriculture; Crop; Production (economics); Agricultural productivity; Extreme Cold; Crop production; Climate change; Climatology; Geography; Biology; Ecology; Forestry","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":[0.0004095751,0.0002114402,0.0001420372,0.0004331917,0.0003543289,0.0007884026,0.0001509452,0.0004063415,0.004425098],"category_scores_gemma":[0.001418036,0.0001061743,0.000289954,0.0005939226,0.0004587476,0.0005136507,0.0006687556,0.0004167183,0.0002836596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006747934,"about_ca_system_score_gemma":0.0004325018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390567,"about_ca_topic_score_gemma":0.02172524,"domain_scores_codex":[0.9998118,0.00004834441,0.00001365337,0.00002258181,0.00002947792,0.0000741106],"domain_scores_gemma":[0.9989833,0.0003063364,0.0003465065,0.00005269686,0.0001538897,0.0001572449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009572993,0.0002327688,0.9267953,0.0001648304,0.0004154174,0.001284123,0.0004873,0.02571485,0.008176981,0.002725061,0.003875036,0.02917092],"study_design_scores_gemma":[0.00001210791,0.00009593327,0.9897268,0.00001523571,0.00007166702,0.0001196688,0.001020967,0.004464893,0.00081176,0.001029787,0.002618596,0.00001269177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913098,0.0003922055,0.0003373564,0.0007815202,0.00005189419,0.000007221073,0.0009138387,0.0000172487,0.006189032],"genre_scores_gemma":[0.9989368,0.0003750712,0.00005016836,0.00004481218,0.00002738703,0.000001922908,0.0001677401,0.00000485448,0.0003912026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01390567,"threshold_uncertainty_score":0.02764946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478943953887366,"score_gpt":0.2502680151704544,"score_spread":0.2254785756315808,"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."}}