{"id":"W4404246294","doi":"10.1017/asb.2024.30","title":"Weekly dynamic motor insurance ratemaking with a telematics signals bonus-malus score","year":2024,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Fonds de recherche du Québec – Nature et technologies; Institució Catalana de Recerca i Estudis Avançats","keywords":"Telematics; Automobile insurance; Actuarial science; Business; Economics; Econometrics; Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007104861,0.0004611021,0.0006368919,0.000722247,0.0003697384,0.001926239,0.001562784,0.002204364,0.006414713],"category_scores_gemma":[0.02551917,0.0003701935,0.0004766999,0.0006190213,0.0006383467,0.00128186,0.0008249772,0.002557975,0.0007042264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001370989,"about_ca_system_score_gemma":0.0006942258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004889297,"about_ca_topic_score_gemma":0.004156613,"domain_scores_codex":[0.9971007,0.001515486,0.0001258095,0.0004961414,0.0004827453,0.0002792813],"domain_scores_gemma":[0.9825633,0.01211269,0.002815416,0.001082534,0.0007725818,0.0006534027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001671744,0.0008639542,0.06836604,0.00009744451,0.0001463064,0.0008357209,0.0002880011,0.8101565,0.002234622,0.02943526,0.005215839,0.08068848],"study_design_scores_gemma":[0.00006654074,0.0003602109,0.01248111,0.00001761779,0.00004063766,0.0001247057,0.00007236809,0.9776754,0.0008036682,0.006859395,0.001461606,0.0000366372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8503816,0.000264459,0.1353616,0.003289057,0.000144032,0.0002358333,0.001377229,0.0004640639,0.008482157],"genre_scores_gemma":[0.9926744,0.00002077514,0.005255357,0.00004227686,0.00003264407,0.0000302184,0.0001451388,0.00001041157,0.001788723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007104861,"threshold_uncertainty_score":0.03757453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.261766194600081,"score_gpt":0.4747040877061348,"score_spread":0.2129378931060538,"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."}}