{"id":"W2327164763","doi":"10.1080/10920277.2005.10596217","title":"“A Bayesian Generalized Linear Model for the Bornhuetter-Ferguson Method of Claims Reserving,” R. J. Verrall, July 2004","year":2005,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized linear model; Bayesian probability; Econometrics; Mathematics; Computer science; Statistics; Applied mathematics","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":[0.02209928,0.001318348,0.001520709,0.001726436,0.001129904,0.001806559,0.005216645,0.002891435,0.009516259],"category_scores_gemma":[0.03604681,0.001452489,0.002163826,0.001665016,0.00276569,0.003425195,0.002177898,0.00497516,0.002572917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527181,"about_ca_system_score_gemma":0.003467908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02300701,"about_ca_topic_score_gemma":0.03096386,"domain_scores_codex":[0.9926248,0.005449914,0.0001615548,0.000580678,0.0009097225,0.000273308],"domain_scores_gemma":[0.9919766,0.005848242,0.0004953836,0.0005906489,0.0009026661,0.0001865973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001840946,0.00007676225,0.001201426,0.0001424729,0.0001719953,0.0001406473,0.0002760577,0.1460448,0.0003330167,0.6995285,0.03784368,0.1140566],"study_design_scores_gemma":[0.00006264629,0.00004601958,0.0006050943,0.00008673248,0.00008338357,0.0001049924,0.00003651332,0.6109247,0.0003176684,0.3707593,0.01688644,0.00008647364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001576281,0.0009276184,0.9936114,0.001936145,0.0002292886,0.00005350376,0.0001286573,0.0001530805,0.001383965],"genre_scores_gemma":[0.09335583,0.003268472,0.8778124,0.001626896,0.001322836,0.0005693808,0.0007265139,0.0004719988,0.02084562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02300701,"threshold_uncertainty_score":0.1168736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07426533128817449,"score_gpt":0.4007642945729233,"score_spread":0.3264989632847489,"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."}}