{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01005598,0.001089467,0.001308843,0.001277557,0.0009728841,0.002401804,0.004261938,0.001968811,0.006621776],"category_scores_gemma":[0.02004233,0.0006392901,0.002467636,0.0009247524,0.001782565,0.00298894,0.001585875,0.004188254,0.0007701981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002144552,"about_ca_system_score_gemma":0.0006715431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01488113,"about_ca_topic_score_gemma":0.01502168,"domain_scores_codex":[0.9971502,0.001296868,0.000125923,0.0006410942,0.0003796426,0.0004062805],"domain_scores_gemma":[0.9858989,0.01027807,0.00153117,0.001053425,0.0007745462,0.0004640074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003840442,0.0003540269,0.02810417,0.0001397619,0.0002499906,0.0005382744,0.001452246,0.6377949,0.001509184,0.2547439,0.00481025,0.06991938],"study_design_scores_gemma":[0.00001918496,0.00005537468,0.00435418,0.00001410024,0.00002907205,0.00006325815,0.0001260914,0.9591231,0.0001905264,0.03497503,0.001019838,0.00003030321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4031311,0.000724966,0.584391,0.001546385,0.0001364843,0.0004477953,0.0006059883,0.0003542305,0.008661955],"genre_scores_gemma":[0.9338517,0.0002206104,0.05622721,0.0001606246,0.00007263458,0.0003318403,0.0007415333,0.00006452944,0.008329323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01488113,"threshold_uncertainty_score":0.05318171,"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."}}