{"id":"W1977165701","doi":"10.1017/asb.2014.11","title":"A POSTERIORI RATEMAKING WITH PANEL DATA","year":2014,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"A priori and a posteriori; Actuarial science; Automobile insurance; Panel data; Set (abstract data type); Econometrics; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004791139,0.0001434774,0.0002673096,0.00007573082,0.0001891316,0.0002891077,0.001737708,0.00006210375,0.001086458],"category_scores_gemma":[0.003268315,0.00008544674,0.0000332262,0.0002764929,0.000151824,0.0001412285,0.000682494,0.0001581073,0.002582231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001081727,"about_ca_system_score_gemma":0.00004815076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007756742,"about_ca_topic_score_gemma":0.00006167198,"domain_scores_codex":[0.9972622,0.0003552441,0.0004394396,0.0007752348,0.0008741174,0.0002937903],"domain_scores_gemma":[0.9961638,0.001377831,0.0001834821,0.001996999,0.0001677177,0.0001101489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008346871,0.000336166,0.05789012,0.0000587268,0.00006970639,0.00007764073,0.002006006,0.002806951,0.001198402,0.01576026,0.2156165,0.7033449],"study_design_scores_gemma":[0.0005598583,0.0001797683,0.01122382,0.00006993125,0.00001503264,0.00005532312,0.0001697491,0.01029645,0.000115204,0.0103968,0.9666325,0.0002855899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5420769,0.0002649729,0.3704755,0.03750149,0.00077033,0.000671376,0.00009084053,0.0002950268,0.04785353],"genre_scores_gemma":[0.9831471,0.00000277845,0.01338401,0.0008087189,0.0001437596,0.000006053314,0.00001058599,0.00001306999,0.002483892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.751016,"threshold_uncertainty_score":0.9998267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2807585817671692,"score_gpt":0.3742323943646587,"score_spread":0.09347381259748949,"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."}}