{"id":"W4280606004","doi":"10.3390/math10101630","title":"Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Categorical variable; Generalized linear model; Variable (mathematics); Econometrics; Variables; Inference; Statistical model; Computer science; Linear model; Statistical inference; Benchmark (surveying); Statistics; Mathematics; Machine learning; Artificial intelligence","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":[0.001304992,0.0001481986,0.0004773637,0.00005305676,0.0001448223,0.0000131662,0.0002517329,0.00004779995,0.0002155733],"category_scores_gemma":[0.002446883,0.0001448743,0.00008908529,0.000290951,0.00003262895,0.00005891483,0.0001290296,0.0001732897,6.522147e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001033463,"about_ca_system_score_gemma":0.00008536193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001765371,"about_ca_topic_score_gemma":0.000005126162,"domain_scores_codex":[0.9983315,0.00008355841,0.0007783753,0.0002071165,0.0003077936,0.0002915817],"domain_scores_gemma":[0.997412,0.00182497,0.0002245415,0.0003570715,0.0001287137,0.00005275662],"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":[0.00001944043,0.0003084337,0.0000427946,0.0005232401,0.00002024531,0.000002708165,0.0004954257,0.01436451,0.0004303851,0.9835972,0.00006087578,0.0001347755],"study_design_scores_gemma":[0.0002868579,0.00002004444,6.799112e-7,0.00003710245,0.00002312313,0.000003859931,0.0001087166,0.4811129,0.000172056,0.5181397,0.00001068386,0.00008431348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02686035,0.00003545762,0.9713796,0.00005062837,0.00005582258,0.0004689402,0.0004781803,0.00003786042,0.0006331698],"genre_scores_gemma":[0.1615001,0.000004470384,0.8381159,0.00001521569,0.00002133479,0.0002468192,0.00001090398,0.00002716915,0.00005808632],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4667484,"threshold_uncertainty_score":0.5907801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1523449623894438,"score_gpt":0.3566988466146037,"score_spread":0.2043538842251598,"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."}}