{"id":"W1989277811","doi":"10.1037/a0034156","title":"Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis.","year":2013,"lang":"en","type":"review","venue":"Journal of Applied Psychology","topic":"Medical Education and Admissions","field":"Medicine","cited_by":260,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Predictive power; Psychology; Meta-analysis; Predictive validity; Personnel selection; Job performance; Selection (genetic algorithm); Applied psychology; Field (mathematics); Social psychology; Computer science; Clinical psychology; Job satisfaction; Statistics; Machine learning; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0401579,0.002469659,0.01114251,0.004330873,0.0006270467,0.003439888,0.001989239,0.002158992,0.002721933],"category_scores_gemma":[0.1094583,0.001284407,0.03074544,0.005794215,0.0008792111,0.002431839,0.001656858,0.002647507,0.0002991631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001971424,"about_ca_system_score_gemma":0.003462891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00579094,"about_ca_topic_score_gemma":0.01201533,"domain_scores_codex":[0.9699116,0.02090003,0.004698445,0.001669112,0.002558365,0.0002624729],"domain_scores_gemma":[0.8940046,0.09474796,0.006548625,0.001674321,0.002557664,0.0004667824],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.005340599,0.00009167719,0.006798313,0.2662541,0.6446633,0.00007370018,0.0001707669,0.0007501205,0.0001380941,0.0002979242,0.00109149,0.07432991],"study_design_scores_gemma":[0.001922084,0.0006460313,0.004818913,0.04516592,0.9427725,0.0001051932,0.00009216798,0.0003582189,0.0002224211,0.0007498426,0.003104704,0.00004205572],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002795022,0.9953882,0.0007325982,0.0002722933,0.0001515411,0.0002054712,0.0002046681,0.00001192814,0.000238268],"genre_scores_gemma":[0.15851,0.8329647,0.004873339,0.0011762,0.0003523388,0.001086192,0.0007376831,0.0000299992,0.0002694903],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9598421,"threshold_uncertainty_score":0.2123778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6729862236868487,"score_gpt":0.6172101001703686,"score_spread":0.05577612351648009,"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."}}