{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001684084,0.0006532453,0.001331432,0.0005871495,0.001863031,0.002812565,0.001679007,0.002660561,0.01773835],"category_scores_gemma":[0.00646594,0.0005052783,0.001366712,0.0005766478,0.003629601,0.003649089,0.003861234,0.003328525,0.001572426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002393846,"about_ca_system_score_gemma":0.001685393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007977829,"about_ca_topic_score_gemma":0.003437575,"domain_scores_codex":[0.9981457,0.0006636315,0.00005663976,0.0002926754,0.0002884038,0.0005529369],"domain_scores_gemma":[0.9967028,0.001452415,0.000529347,0.0002869925,0.0002611143,0.0007672323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002416479,0.0001574326,0.002363802,0.00005430038,0.00002964898,0.000424156,0.00039779,0.05807073,0.0006610833,0.9275435,0.003763637,0.006292212],"study_design_scores_gemma":[0.0001890889,0.0000779588,0.001051038,0.00003758996,0.00001983625,0.0001825129,0.0002211422,0.2175401,0.000221445,0.776494,0.003930967,0.00003424318],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6345224,0.001046952,0.1891803,0.009092004,0.0001844109,0.000183385,0.0006887119,0.000342344,0.1647595],"genre_scores_gemma":[0.9763725,0.000144459,0.003533776,0.0002482684,0.00004658605,0.00006154627,0.0001157136,0.00003123913,0.01944596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01773835,"threshold_uncertainty_score":0.05934066,"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."}}