{"id":"W4285080460","doi":"10.5220/0010601000002993","title":"Estimating Territory Risk Relativity for Auto Insurance Rate Regulation using Generalized Linear Mixed Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Generalized linear model; Econometrics; Computer science; Applied mathematics; Mathematics; Statistics","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":[0.01817464,0.0008996804,0.001497738,0.002375693,0.001114515,0.002562723,0.002626877,0.001715652,0.002498065],"category_scores_gemma":[0.04417162,0.0009458776,0.004153217,0.002465587,0.001104547,0.001609374,0.00254561,0.002041956,0.0004546918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002211398,"about_ca_system_score_gemma":0.002100076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0495064,"about_ca_topic_score_gemma":0.03797229,"domain_scores_codex":[0.9874898,0.008607585,0.0004394744,0.002516507,0.0004428196,0.0005038387],"domain_scores_gemma":[0.9402469,0.04908324,0.005037917,0.003650897,0.001433961,0.0005471217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009072652,0.0005135519,0.6866599,0.0001572008,0.004268527,0.0003019359,0.001052562,0.2350452,0.0006795786,0.01495314,0.001872807,0.05358836],"study_design_scores_gemma":[0.00006240469,0.0003674684,0.113014,0.00005396567,0.0009046357,0.0001214845,0.0008011854,0.8728008,0.0004708058,0.009958114,0.001369792,0.00007536932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023799,0.0002924971,0.09419359,0.0003795664,0.00005757566,0.0001408885,0.0009863505,0.0003041776,0.001265342],"genre_scores_gemma":[0.9701326,0.00006599622,0.02771143,0.00003591647,0.00002664322,0.0001683135,0.0009477619,0.00003839098,0.0008728866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0495064,"threshold_uncertainty_score":0.09843647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0578758002898316,"score_gpt":0.3319912071757398,"score_spread":0.2741154068859082,"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."}}