{"id":"W3206069075","doi":"10.1037/met0000422","title":"Parsimony in model selection: Tools for assessing fit propensity.","year":2023,"lang":"en","type":"article","venue":"PubMed","topic":"Mental Health Research Topics","field":"Psychology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model selection; Equating; Occam's razor; Computer science; Range (aeronautics); Statistical model; Econometrics; Statistical inference; Statistics; Inference; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071823,0.00007367596,0.0001360007,0.0001581439,0.00009784827,0.00008347668,0.0001260894,0.00008594554,0.00005148408],"category_scores_gemma":[0.000246957,0.00007295404,0.00003027945,0.0004799234,0.00002266222,0.0002280383,0.00004972474,0.0001868467,0.0001025603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655901,"about_ca_system_score_gemma":0.0000565146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006177875,"about_ca_topic_score_gemma":0.0001190431,"domain_scores_codex":[0.9985472,0.00009523374,0.0002019073,0.0002650807,0.0001658925,0.0007246673],"domain_scores_gemma":[0.9994271,0.0002064763,0.00003800834,0.0001604144,0.00004270024,0.0001252714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001594511,0.0001531509,0.03044733,0.0001679182,0.00001958167,0.00002383723,0.0004525721,0.0006422742,0.00002058463,0.00289166,0.03570789,0.9293138],"study_design_scores_gemma":[0.001012699,0.00002780828,0.954969,0.00001133661,0.000004008912,0.000005344103,0.000182429,0.02612023,0.0001088446,0.002228229,0.01519323,0.0001368773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677688,0.00004822485,0.000723446,0.003292589,0.0005407388,0.003543697,0.00001288547,0.0001765637,0.02389302],"genre_scores_gemma":[0.9679409,0.000006928714,0.0003911288,0.0003704603,0.0001510991,0.0092549,0.00001643776,0.00002123389,0.02184696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9291769,"threshold_uncertainty_score":0.2974979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5150498753044516,"score_gpt":0.4738799269315013,"score_spread":0.04116994837295024,"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."}}