{"id":"W3024580490","doi":"10.1111/jori.12312","title":"Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies","year":2020,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Crop insurance; Space (punctuation); Econometrics; Actuarial science; Relevance (law); Variety (cybernetics); Property insurance; Economics; Insurance policy; Computer science; Agriculture; Statistics; General insurance; Mathematics; Geography","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":[0.0004312919,0.0001775533,0.0003908863,0.00001620564,0.0003296105,0.0001072668,0.000315381,0.00009022484,0.00000150373],"category_scores_gemma":[0.001266006,0.00007482059,0.00009630738,0.0004845861,0.0000747139,0.001153908,0.0000440049,0.0002144167,0.000003932009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003168241,"about_ca_system_score_gemma":0.00001419036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001910698,"about_ca_topic_score_gemma":0.00005395011,"domain_scores_codex":[0.9984514,0.00007122273,0.0007029177,0.0001761106,0.0003169543,0.0002814546],"domain_scores_gemma":[0.9975058,0.000554386,0.001084555,0.00005211773,0.0005902321,0.0002129352],"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":[0.0005297016,0.00005251681,0.1414111,0.0000537298,0.0000171222,0.00000208496,0.006895128,0.02203456,0.5839247,0.00005674064,0.00004035552,0.2449824],"study_design_scores_gemma":[0.0005371185,0.000927604,0.9709225,0.0001527216,0.00001163562,0.00003296662,0.002042253,0.003179413,0.01762554,0.00008407998,0.004224717,0.000259477],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961293,0.0005068826,0.001521638,0.001062931,0.00005910561,0.0004041829,0.0002763271,0.00002469117,0.00001490744],"genre_scores_gemma":[0.9970312,0.0003769619,0.001949618,0.0003272043,0.0002936004,0.000007258393,0.00000787442,0.000001801705,0.000004487768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8295114,"threshold_uncertainty_score":0.3051095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007000889315120937,"score_gpt":0.2401111123960238,"score_spread":0.2331102230809028,"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."}}