{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1214415,0.004639439,0.003865194,0.01760351,0.002505498,0.006160846,0.004617431,0.002744663,0.0220871],"category_scores_gemma":[0.4246845,0.002187608,0.008980501,0.01474645,0.002640032,0.005517447,0.006975795,0.006898195,0.002468486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0016361,"about_ca_system_score_gemma":0.004387129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002762116,"about_ca_topic_score_gemma":0.005933597,"domain_scores_codex":[0.9143624,0.06882039,0.006591183,0.004614531,0.005055635,0.000555863],"domain_scores_gemma":[0.4558668,0.5127269,0.007844948,0.01478434,0.007132877,0.001644291],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003614153,0.001280119,0.248452,0.008386009,0.03610685,0.001580577,0.008543843,0.06200979,0.001557645,0.04333185,0.1146937,0.4704435],"study_design_scores_gemma":[0.001715305,0.001093881,0.04696054,0.002801217,0.007236287,0.001117292,0.003293809,0.7367816,0.002009445,0.177186,0.01922692,0.0005776293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08171712,0.002047666,0.8830877,0.002652,0.0004850607,0.004211671,0.0105697,0.008950135,0.006279087],"genre_scores_gemma":[0.3304895,0.0004809065,0.6465301,0.0005027765,0.0001428221,0.01232803,0.006551109,0.001971363,0.001003545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8785585,"threshold_uncertainty_score":0.6422517,"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."}}