{"id":"W4410471507","doi":"10.31234/osf.io/rq6yb_v1","title":"Tutorial: How to Generate Missing Data For Simulation Studies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Missing data; Computer science; Data science; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.01005089,0.003098744,0.001849952,0.002590955,0.0007649134,0.002874657,0.002815886,0.002917399,0.1081971],"category_scores_gemma":[0.0706903,0.002001102,0.003136325,0.00264497,0.0007543557,0.004680281,0.002117404,0.006011603,0.03631425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000943204,"about_ca_system_score_gemma":0.001831103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377857,"about_ca_topic_score_gemma":0.002305154,"domain_scores_codex":[0.9949299,0.003320018,0.000444037,0.0003017363,0.000892269,0.0001120146],"domain_scores_gemma":[0.9511561,0.0412755,0.0009552243,0.001759175,0.004144178,0.0007098909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002341747,0.0002980562,0.0009879828,0.004238678,0.0003551376,0.0007034073,0.0005269629,0.03331643,0.002182075,0.1086063,0.4144518,0.434099],"study_design_scores_gemma":[0.0002928874,0.0001206909,0.0006042613,0.001506993,0.0001498208,0.001079587,0.0001548127,0.09248605,0.002908366,0.2843353,0.6161703,0.0001909228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003278103,0.001859879,0.9791254,0.002475019,0.001157154,0.000374322,0.001753407,0.006686549,0.006240468],"genre_scores_gemma":[0.004969185,0.004003021,0.9714675,0.001642874,0.0009504578,0.001244657,0.002130238,0.003172287,0.0104198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1081971,"threshold_uncertainty_score":0.3619552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6986255797806713,"score_gpt":0.5893709231828437,"score_spread":0.1092546565978276,"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."}}