{"id":"W4389432236","doi":"10.3390/risks11120213","title":"The Applications of Generalized Poisson Regression Models to Insurance Claim Data","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Count data; Poisson regression; Negative binomial distribution; Poisson distribution; Zero-inflated model; Covariate; Generalized linear model; Econometrics; Computer science; Zero (linguistics); Mathematics; Statistics; Medicine; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0166515,0.00178611,0.002334173,0.003434255,0.0007705013,0.002275012,0.004876632,0.00292799,0.002251561],"category_scores_gemma":[0.05343009,0.001196862,0.003771452,0.005418845,0.002109471,0.00341049,0.002741003,0.004528206,0.000830549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628609,"about_ca_system_score_gemma":0.001632308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007915008,"about_ca_topic_score_gemma":0.004737,"domain_scores_codex":[0.9906706,0.006236614,0.0003933745,0.001212824,0.001201362,0.0002852093],"domain_scores_gemma":[0.968921,0.02513123,0.00251131,0.001902682,0.001271374,0.0002624302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006230606,0.00009898888,0.01013847,0.000513868,0.0003876243,0.0005376815,0.0006836939,0.459496,0.0007021379,0.4331356,0.003984036,0.09025963],"study_design_scores_gemma":[0.00001330931,0.00003888592,0.001127021,0.00007640973,0.00004069523,0.0001601025,0.0000825856,0.7601103,0.0001821774,0.2353967,0.002728654,0.00004322938],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01506576,0.002396786,0.9789203,0.001372222,0.0001330211,0.00008532725,0.0003198831,0.0002581222,0.001448564],"genre_scores_gemma":[0.5962325,0.01184822,0.3799098,0.001038645,0.001286249,0.0007830812,0.002169902,0.0003270315,0.006404547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0166515,"threshold_uncertainty_score":0.08806258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.492465044895535,"score_gpt":0.4916763516318289,"score_spread":0.0007886932637061195,"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."}}