{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003989523,0.00009212454,0.0002025997,0.0001172934,0.0004002626,0.00009528099,0.002504464,0.00007250918,0.00001590539],"category_scores_gemma":[0.0007263311,0.00004731506,0.00006035007,0.001514753,0.0001115263,0.0002941855,0.0008116333,0.0001155251,0.0003734718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001417134,"about_ca_system_score_gemma":0.00006622807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003026308,"about_ca_topic_score_gemma":0.0002458614,"domain_scores_codex":[0.9975814,0.0002194537,0.0005328593,0.0005154228,0.0009282265,0.0002226036],"domain_scores_gemma":[0.9955855,0.001072427,0.0001827525,0.00283168,0.0002293399,0.00009830162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001559458,0.00009961281,0.003493156,0.00001094756,0.00002198895,0.000001465204,0.001771786,0.07706041,0.001620561,0.0586318,0.1168443,0.740288],"study_design_scores_gemma":[0.0001965113,0.00001817835,0.006651602,0.00001832639,0.000005338014,8.175423e-7,0.0002118903,0.2097204,0.0007161524,0.6823893,0.09996484,0.0001066252],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7046271,0.001525891,0.2743595,0.0128769,0.0004452014,0.001464863,0.0007667748,0.0002161507,0.00371766],"genre_scores_gemma":[0.9946181,0.0006247442,0.002464487,0.000112209,0.00006189325,0.00008308107,0.00002686,0.000008637023,0.001999955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7401814,"threshold_uncertainty_score":0.4800349,"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."}}