{"id":"W4382516807","doi":"10.31234/osf.io/p2n8a","title":"Comparing the Accuracy of Three Predictive Information Criteria for Bayesian Linear Multilevel Model Selection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Overfitting; Deviance information criterion; Akaike information criterion; Bayesian information criterion; Model selection; Information Criteria; Leverage (statistics); Computer science; Multilevel model; Bayesian probability; Selection (genetic algorithm); Deviance (statistics); Data mining; Context (archaeology); Linear model; Machine learning; Artificial intelligence; Econometrics; Statistics; Bayesian inference; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1141385,0.002323742,0.00341777,0.009712989,0.001775195,0.006059015,0.004725027,0.003630946,0.002875306],"category_scores_gemma":[0.3381397,0.001092503,0.004241318,0.006364891,0.003042704,0.006024958,0.004716939,0.005566468,0.0006601234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0044339,"about_ca_system_score_gemma":0.005367716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01128956,"about_ca_topic_score_gemma":0.01189198,"domain_scores_codex":[0.9401161,0.04230307,0.004451827,0.003566235,0.008522454,0.001040374],"domain_scores_gemma":[0.5136784,0.4413697,0.01100839,0.01191302,0.01944628,0.002584149],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003001346,0.0004319579,0.07002166,0.002950223,0.005971494,0.000348789,0.00216434,0.443429,0.0009475187,0.1319642,0.01112051,0.327649],"study_design_scores_gemma":[0.0003586207,0.0005998403,0.01361156,0.0009412352,0.0009657008,0.0001648855,0.0004995664,0.8941292,0.00154487,0.0840165,0.0028725,0.0002954781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1488826,0.01020431,0.8222618,0.004367699,0.0003344284,0.0007995376,0.001815184,0.00186622,0.009468331],"genre_scores_gemma":[0.6446968,0.00217028,0.346257,0.0007744144,0.0001933104,0.000898872,0.003457668,0.0005995692,0.0009521218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8858615,"threshold_uncertainty_score":0.6036293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2668533767769755,"score_gpt":0.4405836038420233,"score_spread":0.1737302270650479,"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."}}