{"id":"W6977304029","doi":"10.6084/m9.figshare.25201939","title":"Additional file 1 of Model-based standardization using multiple imputation","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; University of Toronto","funders":"","keywords":"Standardization; Imputation (statistics); Missing data; Data file","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002534741,0.00006364457,0.00009113597,0.00004962481,0.00003103571,0.00003116692,0.00004205737,0.00004563762,0.9810275],"category_scores_gemma":[0.01227477,0.00005845784,0.0000431733,0.0001365814,0.000005792713,0.00006615094,0.00001380244,0.00005456337,0.0001151784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003401397,"about_ca_system_score_gemma":0.0001926548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.461077e-7,"about_ca_topic_score_gemma":8.111925e-7,"domain_scores_codex":[0.9994666,0.00002764201,0.0001524912,0.000114148,0.0001614117,0.00007769325],"domain_scores_gemma":[0.9941698,0.005531474,0.00005323096,0.00007251481,0.0001450607,0.00002786347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002739176,0.00001064143,6.973358e-8,0.0003774508,0.000005198091,0.000002220772,0.00001666976,0.0007720086,0.00001528943,0.001648642,0.9863673,0.01078181],"study_design_scores_gemma":[0.00003841951,0.00001190954,0.000007342803,0.002881069,0.00000616642,9.205075e-7,0.000003514382,0.8723134,0.00017248,0.1001312,0.02437084,0.00006274667],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[5.157312e-7,0.000003458452,0.4461139,0.000002245689,0.00000654806,0.00004713692,0.5534483,0.00003219584,0.000345735],"genre_scores_gemma":[0.0008604383,1.766695e-8,0.5150217,0.000008281439,0.00002412193,0.00008012754,0.4839748,0.00000910076,0.0000213006],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.9809124,"threshold_uncertainty_score":0.9960452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350658963163708,"score_gpt":0.3860245751603788,"score_spread":0.250958678844008,"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."}}