{"id":"W1818080627","doi":"10.1111/jori.12038","title":"Separation Without Exclusion in Financial Insurance","year":2014,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Actuarial science; Separation (statistics); Default risk; Business; Auto insurance risk selection; Insurance policy; Uncorrelated; Financial risk; sort; Economics; General insurance; Credit risk; Statistics; Computer science; Mathematics","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":[0.002161854,0.0002220753,0.0007420062,0.0004746843,0.0001479746,0.00006090099,0.0003997278,0.0001501407,0.00003023861],"category_scores_gemma":[0.0008730291,0.0002321675,0.0002140515,0.0005671253,0.00006427234,0.0007271222,0.00004633745,0.0005280474,0.0001855944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001899073,"about_ca_system_score_gemma":0.00003998421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002234203,"about_ca_topic_score_gemma":0.0002160409,"domain_scores_codex":[0.997616,0.00007547789,0.001488144,0.0003094764,0.0001465545,0.0003643329],"domain_scores_gemma":[0.9975876,0.00008580909,0.001794943,0.0003132645,0.0001377304,0.00008067142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000214081,0.0001505317,0.9410859,0.00002848388,0.00001120806,0.00001205753,0.0006591563,0.002201329,0.00003830366,0.03515967,0.0005472347,0.01989207],"study_design_scores_gemma":[0.001664712,0.000171562,0.8928083,0.0001105987,0.00000371508,0.00000972265,0.00001627777,0.0007599997,0.00007753022,0.03232013,0.07180537,0.0002520689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758953,0.002376271,0.01538518,0.0002368641,0.001254974,0.0001696625,0.00004836797,0.00001515427,0.004618272],"genre_scores_gemma":[0.994292,0.003334082,0.001353272,0.0003327637,0.0005489202,0.000008353488,0.000002275823,0.00002511153,0.000103223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07125813,"threshold_uncertainty_score":0.9467515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324835784209055,"score_gpt":0.2349960781531544,"score_spread":0.2217477203110639,"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."}}