{"id":"W4288717471","doi":"10.3390/risks10080152","title":"Multiple Bonus–Malus Scale Models for Insureds of Different Sizes","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credibility; Credibility theory; A priori and a posteriori; Scale (ratio); Computer science; Quality (philosophy); Product (mathematics); Actuarial science; Econometrics; Class (philosophy); Mathematics; Business; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00033226,0.0001207212,0.0003843839,0.0001549698,0.000209609,0.0000148954,0.0002478256,0.00004156574,0.00009313066],"category_scores_gemma":[0.00003387892,0.0001403401,0.0001863573,0.000151747,0.00003014849,0.00009684859,0.0001514325,0.0001107494,0.00001561659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008920726,"about_ca_system_score_gemma":0.000009507593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005114105,"about_ca_topic_score_gemma":0.00006083261,"domain_scores_codex":[0.9989089,0.00001109485,0.0004791651,0.0002896634,0.00005575665,0.0002553926],"domain_scores_gemma":[0.9993148,0.00005104637,0.00028642,0.000289737,0.00002373114,0.00003421689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004190817,0.001086873,0.3230691,0.0002076144,0.000151849,0.000003408912,0.002726241,0.03385874,0.00008750123,0.6087912,0.005479726,0.02411864],"study_design_scores_gemma":[0.004411021,0.0008084832,0.2810984,0.00001846073,0.00003524328,0.000001457459,0.0006109881,0.1442076,0.0008603138,0.4049195,0.1622078,0.0008207781],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604006,0.001876821,0.02488231,0.000174252,0.0007215209,0.0006457696,0.00132297,0.00003668361,0.009939057],"genre_scores_gemma":[0.9978917,0.0002165681,0.0005052632,0.000103864,0.00006691692,0.0003138835,0.00003459417,0.00002411451,0.0008430756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2038717,"threshold_uncertainty_score":0.5722902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06246705572382515,"score_gpt":0.2441139530838075,"score_spread":0.1816468973599824,"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."}}