{"id":"W3121957211","doi":"","title":"Testing for Evidence of Adverse Selection in the Automobile Insurance Market: A Comment","year":2001,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; HEC Montréal","funders":"","keywords":"Adverse selection; Deductible; Actuarial science; Portfolio; Automobile insurance; Selection (genetic algorithm); Business; Economics; Computer science; Financial economics; Artificial intelligence","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":[0.09373648,0.001192807,0.002912698,0.002581833,0.003070967,0.005731448,0.0110516,0.02443855,0.01497328],"category_scores_gemma":[0.4883801,0.0008045806,0.004306802,0.003318134,0.01707803,0.01599126,0.006520856,0.01860591,0.003109856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002195002,"about_ca_system_score_gemma":0.003896397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038929,"about_ca_topic_score_gemma":0.004479521,"domain_scores_codex":[0.9512942,0.02021931,0.005040537,0.009435305,0.01223458,0.001776047],"domain_scores_gemma":[0.2205874,0.7152029,0.01633948,0.0201148,0.02503089,0.002724558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00172701,0.0004019291,0.1367574,0.00202307,0.001677291,0.004695041,0.005335573,0.004479671,0.002629433,0.4824755,0.2834764,0.0743217],"study_design_scores_gemma":[0.001069598,0.0005353621,0.04437301,0.001224768,0.0006986102,0.002798766,0.003053878,0.01684301,0.004011909,0.8541958,0.07081944,0.0003758316],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.04038257,0.003212793,0.015869,0.9193127,0.00354314,0.00008441566,0.001024871,0.0002404026,0.01633001],"genre_scores_gemma":[0.5557693,0.002883505,0.01133945,0.3990639,0.02325204,0.0003342161,0.0005357531,0.0001945164,0.006627157],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.09373648,"threshold_uncertainty_score":0.4957318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902969281191869,"score_gpt":0.2477372504709713,"score_spread":0.2187075576590526,"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."}}