{"id":"W2086012744","doi":"10.1037/a0029253","title":"Individual influence on model selection.","year":2012,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Akaike information criterion; Bayesian information criterion; Model selection; Selection (genetic algorithm); Ranking (information retrieval); Generality; Information Criteria; Computer science; Bayesian probability; Deviance information criterion; Multilevel model; Econometrics; Bayesian inference; Statistics; Data mining; Machine learning; Artificial intelligence; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.1461558,0.001819003,0.002111054,0.00376775,0.001824561,0.004714647,0.00234369,0.002227333,0.004834996],"category_scores_gemma":[0.546576,0.001010445,0.003325469,0.002495131,0.00533899,0.005017966,0.005847765,0.005408008,0.0006816306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002600035,"about_ca_system_score_gemma":0.003105416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003126483,"about_ca_topic_score_gemma":0.003934463,"domain_scores_codex":[0.7725892,0.189969,0.005409789,0.01042848,0.02025232,0.001351309],"domain_scores_gemma":[0.3532228,0.5925876,0.01076056,0.03349983,0.008301547,0.001627601],"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":[0.0008853195,0.0003185941,0.1033166,0.001590566,0.00515295,0.001309183,0.008750361,0.07748014,0.002613011,0.4569933,0.01009495,0.3314951],"study_design_scores_gemma":[0.0001285386,0.0004652101,0.0181313,0.000561435,0.000978865,0.000822706,0.001083405,0.3957482,0.004228465,0.5627245,0.01492658,0.0002007958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07725093,0.002555304,0.8895614,0.004008966,0.0005734379,0.0008003982,0.0002841616,0.0008346066,0.02413066],"genre_scores_gemma":[0.7324887,0.0008589068,0.2619889,0.001269313,0.000386455,0.0008661279,0.0002918573,0.0004299427,0.001419694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1461558,"threshold_uncertainty_score":0.7729552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3007814790311631,"score_gpt":0.5548223446057223,"score_spread":0.2540408655745592,"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."}}