{"id":"W4403230631","doi":"10.1515/apjri-2024-0002","title":"Gaussian Mixture Regression Model with Sparsity for Clustering of Territory Risk in Auto Insurance","year":2024,"lang":"en","type":"article","venue":"Asia-Pacific Journal of Risk and Insurance","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Mixture model; Regression; Computer science; Econometrics; Logistic regression; Regression analysis; Statistics; Mathematics; 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":[0.005339385,0.001013222,0.001566423,0.001424331,0.0005885359,0.001612144,0.002404327,0.0015773,0.002310597],"category_scores_gemma":[0.01535098,0.0007649073,0.001861788,0.001484128,0.001344075,0.001672343,0.00147386,0.00219606,0.0005979773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513777,"about_ca_system_score_gemma":0.001140898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02109572,"about_ca_topic_score_gemma":0.01324958,"domain_scores_codex":[0.9970042,0.001498402,0.0001292082,0.000737605,0.000391608,0.0002389208],"domain_scores_gemma":[0.9929193,0.00434591,0.0008702478,0.0006379276,0.001044616,0.0001820738],"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":[0.0001631017,0.00007828468,0.007849202,0.0001194919,0.0001584903,0.0001490415,0.0002563013,0.9051207,0.001562816,0.04560031,0.001889873,0.03705244],"study_design_scores_gemma":[0.000002843102,0.000006386092,0.0004744752,0.000005270147,0.000007793483,0.00000805641,0.00001177163,0.9962797,0.00010692,0.002933473,0.0001559824,0.000007380805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04259765,0.0002302001,0.9555506,0.000308231,0.00003097482,0.00005281149,0.0001609456,0.0002700674,0.0007986332],"genre_scores_gemma":[0.8362485,0.0004770498,0.1570174,0.0001338197,0.00009176668,0.0002075011,0.0009927835,0.0001239819,0.004707191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02109572,"threshold_uncertainty_score":0.04194587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333080440173405,"score_gpt":0.2142398018763244,"score_spread":0.2009089974745903,"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."}}