{"id":"W2053061982","doi":"10.1093/biomet/asn034","title":"Extended Bayesian information criteria for model selection with large model spaces","year":2008,"lang":"en","type":"article","venue":"Biometrika","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2051,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; National University of Singapore","keywords":"Chen; Model selection; Selection (genetic algorithm); Bayesian probability; Mathematics; Library science; Bayesian inference; Statistics; Deviance information criterion; Bayesian information criterion; Information retrieval; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0436078,0.001966303,0.003512454,0.003626946,0.00126171,0.003758326,0.003673317,0.002843164,0.003372648],"category_scores_gemma":[0.106996,0.001260695,0.002293913,0.003112921,0.003499785,0.004593279,0.005157629,0.00466009,0.0009118618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218717,"about_ca_system_score_gemma":0.002986663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00284447,"about_ca_topic_score_gemma":0.002066962,"domain_scores_codex":[0.9622844,0.02807961,0.001398499,0.001795321,0.005803204,0.00063891],"domain_scores_gemma":[0.8886174,0.09665219,0.002872024,0.005154181,0.005872324,0.0008318739],"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":[0.0001928034,0.00006959782,0.001661269,0.0005051742,0.0005479361,0.0003720518,0.0002747622,0.3248659,0.001012058,0.5614723,0.003750879,0.1052752],"study_design_scores_gemma":[0.00005760074,0.00007087344,0.0004328557,0.00008329447,0.00005264009,0.0001216012,0.00002009186,0.549605,0.000324832,0.4459066,0.003262528,0.00006198746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002213713,0.0007571667,0.9956021,0.0002673588,0.00003375639,0.00006380346,0.0000968183,0.0001073905,0.0008577601],"genre_scores_gemma":[0.1736832,0.002265239,0.8178002,0.0008443728,0.000496263,0.00127401,0.0009407163,0.0002528508,0.002443174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0436078,"threshold_uncertainty_score":0.2306228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898004681725513,"score_gpt":0.264658169572034,"score_spread":0.2456781227547789,"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."}}