{"id":"W4387573002","doi":"10.3390/sym15101905","title":"Model Selection in Generalized Linear Models","year":2023,"lang":"en","type":"article","venue":"Symmetry","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Generalized linear model; Negative binomial distribution; Model selection; Mathematics; Wald test; Selection (genetic algorithm); Poisson regression; Binomial regression; Statistics; Poisson distribution; Likelihood-ratio test; Regression analysis; Linear regression; Linear model; Count data; Binomial (polynomial); Stepwise regression; Statistical hypothesis testing; Computer science; Population; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004299444,0.00009105414,0.0001816121,0.0001720221,0.00003548454,0.00001324916,0.00008415642,0.00008309466,0.00004149498],"category_scores_gemma":[0.0005436395,0.000081792,0.00003723364,0.0006923706,0.00001766929,0.00006527753,0.00003560059,0.0001430863,0.00005802501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003360212,"about_ca_system_score_gemma":0.0000325058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003174915,"about_ca_topic_score_gemma":0.00001662575,"domain_scores_codex":[0.9991395,0.0000804391,0.0002172072,0.0001810409,0.0001521481,0.0002296174],"domain_scores_gemma":[0.9994182,0.0003563845,0.00003492807,0.000106606,0.00003452176,0.0000493811],"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":[0.000009960246,0.00003072292,0.0001928137,0.00004239223,0.000005998148,0.000004827509,0.0001007065,0.0009318675,0.0007858141,0.9877772,0.001751327,0.008366361],"study_design_scores_gemma":[0.000118568,0.000007226259,0.00008920773,0.00001041547,0.000003548693,7.514034e-7,0.000009622603,0.490392,0.0002352413,0.5090715,0.000008443599,0.00005345761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05365442,0.000007714637,0.9405655,0.0001005291,0.00005843398,0.0001046848,0.00001271408,0.0001677617,0.005328205],"genre_scores_gemma":[0.1984878,0.00001556323,0.8007471,0.00007279165,0.00005142593,0.00002223651,0.000003121646,0.00001932138,0.0005805695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4894602,"threshold_uncertainty_score":0.3335381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444628970261127,"score_gpt":0.4047763052497834,"score_spread":0.2603134082236707,"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."}}