{"id":"W3012435887","doi":"10.1111/1467-8489.12368","title":"Calibration of agricultural risk programming models using positive mathematical programming","year":2020,"lang":"en","type":"article","venue":"Australian Journal of Agricultural and Resource Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Social Sciences and Humanities Research Council of Canada; International Association for Applied Econometrics; University of Victoria","keywords":"Calibration; Sensitivity (control systems); Function (biology); Logarithm; Exponential function; Mathematical optimization; Computer science; Econometrics; Cropping; Agriculture; Statistics; Mathematics; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002314223,0.0003045095,0.0005520436,0.00001717719,0.0002716396,0.0001619974,0.0003251059,0.0002016502,0.00002289697],"category_scores_gemma":[0.00005425291,0.00009645394,0.0003131371,0.0003146441,0.0002092643,0.0007118665,0.00009586962,0.0004173605,0.000001370812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255044,"about_ca_system_score_gemma":0.00001388275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006593155,"about_ca_topic_score_gemma":0.00001951389,"domain_scores_codex":[0.9980713,0.0001222055,0.0008969409,0.0002974852,0.0002318202,0.0003802764],"domain_scores_gemma":[0.9981011,0.0001195445,0.001094783,0.00003713198,0.0002259634,0.0004214846],"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":[0.000862152,0.001143782,0.04968102,0.0004927057,0.001369997,0.0000627016,0.02013005,0.1576386,0.5448825,0.00443222,0.002638995,0.2166652],"study_design_scores_gemma":[0.001654693,0.004330113,0.8687786,0.0007447144,0.0008485465,0.002095599,0.07027137,0.003097685,0.04269302,0.001096498,0.002489246,0.001899937],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967434,0.0001981262,0.00005711107,0.002537352,0.00005006945,0.0003086497,0.00003926642,0.00002208999,0.00004391543],"genre_scores_gemma":[0.9963809,0.0001643679,0.002816631,0.00004587527,0.0004820145,0.000002113683,0.00002695262,0.000002210081,0.0000789258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8190976,"threshold_uncertainty_score":0.3933277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959990514052914,"score_gpt":0.2149271059303301,"score_spread":0.185327200789801,"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."}}