{"id":"W2224720476","doi":"10.1177/0049124115610345","title":"Obtaining Predictions from Models Fit to Multiply Imputed Data","year":2015,"lang":"en","type":"article","venue":"Sociological Methods & Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Imputation (statistics); Set (abstract data type); Data set; Data mining; Missing data; Machine learning; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02050031,0.0002320971,0.0005882097,0.0001489859,0.0003545324,0.0001264316,0.001603009,0.000408734,0.000311196],"category_scores_gemma":[0.07837649,0.0001691057,0.00007579942,0.0006686279,0.0005215381,0.0001943097,0.002126444,0.001383543,0.0001173827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257137,"about_ca_system_score_gemma":0.0002584091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002717894,"about_ca_topic_score_gemma":0.000007221865,"domain_scores_codex":[0.9881436,0.008515161,0.0005641329,0.0009952203,0.0008611418,0.000920717],"domain_scores_gemma":[0.9612074,0.03568321,0.00007596385,0.001459161,0.0007183636,0.0008559168],"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":[0.0002065108,0.000334794,0.0003940795,0.00003054328,0.0001408568,0.00004590457,0.005363302,0.0000686511,0.001050608,0.6229985,0.0240324,0.3453339],"study_design_scores_gemma":[0.0003210211,0.0002976872,0.0004859488,0.00003470146,0.00002121301,0.000002094724,0.001896793,0.1199183,0.00006347954,0.8747186,0.002050759,0.0001893942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004968429,0.0001384828,0.9869233,0.001456539,0.000232372,0.0006082184,0.0005043481,0.0002328448,0.004935415],"genre_scores_gemma":[0.02937941,0.00001947329,0.9695984,0.0001879093,0.0004721112,0.0001498076,0.00003551798,0.00003087829,0.0001265652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3451445,"threshold_uncertainty_score":0.9293867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8988619494689873,"score_gpt":0.6764142898064175,"score_spread":0.2224476596625699,"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."}}