{"id":"W3162338773","doi":"10.1002/sta4.387","title":"A constrained minimum method for model selection","year":2021,"lang":"en","type":"article","venue":"Stat","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model selection; Consistency (knowledge bases); Selection (genetic algorithm); Bayesian information criterion; Computer science; Information Criteria; Mathematical optimization; Mathematics; Bayesian probability; Sample size determination; Algorithm; Artificial intelligence; Statistics","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":[0.0003051422,0.0000715606,0.0001577168,0.00001673908,0.0000508286,0.00002038501,0.00003597769,0.00004329735,0.0001849343],"category_scores_gemma":[0.002869077,0.00006439709,0.00004824911,0.00008361648,0.00002153593,0.00002168104,0.00001493438,0.00005977931,0.000003530301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001770085,"about_ca_system_score_gemma":0.0001158126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002371455,"about_ca_topic_score_gemma":0.000008698247,"domain_scores_codex":[0.9993601,0.00007813478,0.0001590406,0.0001621827,0.0000814351,0.0001591325],"domain_scores_gemma":[0.9978554,0.001780505,0.00003857079,0.00009599546,0.0001786599,0.00005090218],"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":[0.00001870128,0.00004298999,0.000005275469,0.00008139291,0.0000202612,0.000001888008,0.0001765892,0.00002240054,0.01139874,0.9337646,0.004249696,0.05021743],"study_design_scores_gemma":[0.0001972418,0.00002574867,0.000002568288,0.000007928494,0.00002070896,0.000005483416,0.00006867046,0.3964771,0.006682743,0.5960116,0.0004453918,0.00005485893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008083056,0.00001045531,0.9931031,0.0002361944,0.00007036312,0.0001328379,0.0001116282,0.00004085544,0.00548623],"genre_scores_gemma":[0.005017283,0.000002425643,0.9929144,0.0001766223,0.0000338752,0.00003782539,0.000005594028,0.00001222874,0.001799777],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3964547,"threshold_uncertainty_score":0.343476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484828444350864,"score_gpt":0.4531084936834141,"score_spread":0.3046256492483277,"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."}}