{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006771559,0.001960123,0.002562,0.002871784,0.001260632,0.001544673,0.003967592,0.002353459,0.00696742],"category_scores_gemma":[0.02431908,0.001388542,0.002887182,0.002545065,0.001571074,0.002095708,0.002960771,0.004617352,0.002172747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123903,"about_ca_system_score_gemma":0.003290111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006309779,"about_ca_topic_score_gemma":0.005252701,"domain_scores_codex":[0.9946996,0.003665241,0.0001571525,0.0004948481,0.0008309515,0.000152267],"domain_scores_gemma":[0.9888115,0.009116101,0.0004089205,0.0005948442,0.0009091205,0.0001594989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001408994,0.0001270719,0.001062469,0.0005734811,0.0005805047,0.0003319706,0.0002231474,0.5872761,0.003221782,0.2026534,0.01209882,0.1917105],"study_design_scores_gemma":[0.00003704143,0.00003748454,0.0001219842,0.00004472825,0.00003637977,0.00006242989,0.00001359171,0.9335176,0.0005480118,0.06086212,0.004690994,0.00002763728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002354244,0.00008501095,0.9991981,0.00007257731,0.00002014753,0.00002520479,0.00003018262,0.0001018409,0.0002314984],"genre_scores_gemma":[0.02212007,0.0003076109,0.9745256,0.0002442223,0.0001060478,0.0006209105,0.0003248452,0.0003700181,0.001380714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00696742,"threshold_uncertainty_score":0.0358119,"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."}}