{"id":"W2922973825","doi":"10.1017/asb.2019.5","title":"INDEX INSURANCE DESIGN","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Nanyang Technological University","keywords":"Indemnity; Index (typography); Basis risk; Insurance policy; Actuarial science; Uniqueness; Monotonic function; Function (biology); Mathematical optimization; Econometrics; Computer science; Mathematics; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"category_scores_codex":[0.0001388042,0.0001155387,0.0001194622,0.000003947926,0.0001031423,0.00003504441,0.0002578143,0.000072505,0.003506814],"category_scores_gemma":[0.0000400404,0.00003304328,0.0000524611,0.0001900516,0.00003379067,0.00003341482,0.00005706131,0.0001165021,0.004017214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009763638,"about_ca_system_score_gemma":0.000003101052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001201961,"about_ca_topic_score_gemma":0.00001262763,"domain_scores_codex":[0.9991074,0.00006395708,0.000131451,0.0002573041,0.0001848515,0.0002549867],"domain_scores_gemma":[0.9995674,0.0002188524,0.000055195,0.00004483476,0.00004156087,0.00007218538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008381128,0.00007520624,0.6407584,0.000007267441,0.000008545516,0.00001007571,0.00005806352,0.0005598904,0.1896366,0.0002004389,0.03236332,0.1362384],"study_design_scores_gemma":[0.00008725798,0.0001213329,0.8509113,0.00001718088,0.000001218937,0.00000816813,0.00008728502,0.00003520814,0.001796115,0.00005360394,0.146733,0.0001483478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931139,0.0001471264,0.00003278082,0.002122886,0.0001403285,0.0002148365,0.00000460201,0.00007846378,0.004145122],"genre_scores_gemma":[0.9930311,0.00003800908,0.0002446222,0.0003109954,0.0001417436,0.000009558757,0.000006260444,5.45865e-7,0.006217103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2101529,"threshold_uncertainty_score":0.9974041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00909297331177362,"score_gpt":0.176805861729796,"score_spread":0.1677128884180223,"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."}}