{"id":"W4416888374","doi":"10.1080/10920277.2025.2587884","title":"An Effective Bayesian GLM Approach for IBNR Claim Count Estimation","year":2025,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian probability; Estimation; Bayes estimator; Stability (learning theory); Equating; Estimation theory","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005590977,0.0002004563,0.0003060341,0.0003023789,0.0004041734,0.0005492314,0.001244741,0.00005388474,0.000003549956],"category_scores_gemma":[0.0002061761,0.0001823402,0.00009895141,0.0008039008,0.0001594465,0.001068064,0.00007408964,0.0003426383,0.000003388033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002945335,"about_ca_system_score_gemma":0.0003130163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005119678,"about_ca_topic_score_gemma":0.00001929035,"domain_scores_codex":[0.9982907,0.0001933861,0.0003986142,0.0004390086,0.0003223373,0.0003559413],"domain_scores_gemma":[0.9982422,0.0001881767,0.0004642383,0.0006532025,0.0002857728,0.0001663977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001226077,0.0001891966,0.002703843,0.00001548203,0.00005521323,0.000002668732,0.0002719205,0.001062131,0.0005183634,0.009602263,0.00448681,0.9809695],"study_design_scores_gemma":[0.001270176,0.001074835,0.0872072,0.00002717325,0.00005583806,0.00007513315,0.0001025122,0.8947757,0.002914604,0.004775176,0.007231109,0.0004905727],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004022887,0.000008973617,0.9935139,0.0004957358,0.0004049217,0.0006804568,0.00002625012,0.0002700783,0.0005767641],"genre_scores_gemma":[0.5403187,0.000008102195,0.4587784,0.0005271687,0.0001861285,0.0001060813,0.00005335255,0.000009105533,0.00001295687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9804789,"threshold_uncertainty_score":0.7435616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007622397368888987,"score_gpt":0.2881737559907953,"score_spread":0.2805513586219063,"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."}}