{"id":"W4285735718","doi":"10.1007/s10584-022-03404-0","title":"On the relative importance of climatic and non-climatic factors in crop yield models","year":2022,"lang":"en","type":"article","venue":"Climatic Change","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Climate change; Climate model; Environmental science; Crop yield; Precipitation; Statistical model; Variable (mathematics); Climatology; Statistics; Yield (engineering); Mean squared error; Econometrics; Mathematics; Meteorology; Ecology; Geography; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005977976,0.0002698493,0.0004371153,0.00004245215,0.0002998168,0.00003393546,0.0003820492,0.00008346282,0.00201227],"category_scores_gemma":[0.0002944062,0.00009218429,0.0001042239,0.0008546088,0.0001095214,0.0002539579,0.0003040138,0.0003793205,0.00001054409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061748,"about_ca_system_score_gemma":0.000005968854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002913684,"about_ca_topic_score_gemma":0.001194576,"domain_scores_codex":[0.9981613,0.0001613543,0.0005141568,0.0003364877,0.0003926482,0.0004340309],"domain_scores_gemma":[0.997715,0.001582319,0.0003984436,0.0001618014,0.00004493592,0.00009753049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007105122,0.005792579,0.5354065,0.002357751,0.0003864586,0.0001903786,0.289885,0.0002445188,0.09085385,0.0565081,0.00861515,0.009049232],"study_design_scores_gemma":[0.0008180316,0.002464824,0.8616275,0.0009026391,0.0001254377,0.00003633445,0.05843304,0.009154809,0.0007051601,0.06451473,0.0000998808,0.001117634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935659,0.0002869785,0.000001354406,0.00248903,0.00008943214,0.001101035,0.0002447375,0.00003148962,0.002190083],"genre_scores_gemma":[0.998529,0.0001235047,0.00003159138,0.0008480066,0.00003799147,0.0002934223,0.00006972939,0.000003802088,0.00006293365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.326221,"threshold_uncertainty_score":0.9989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1431560906751017,"score_gpt":0.2601672461663188,"score_spread":0.117011155491217,"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."}}