{"id":"W2348932631","doi":"10.1016/j.prevetmed.2016.04.003","title":"Multiple imputation in veterinary epidemiological studies: a case study and simulation","year":2016,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Imputation (statistics); Veterinary medicine; Statistics; Epidemiology; Missing data; Mathematics; 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":[0.08616184,0.0009188801,0.003017568,0.001847914,0.001356495,0.003139724,0.004205911,0.005913698,0.004097128],"category_scores_gemma":[0.2150733,0.001245748,0.003901627,0.003321577,0.001854787,0.003059022,0.001951626,0.003813679,0.0003456685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001384413,"about_ca_system_score_gemma":0.001997599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00701495,"about_ca_topic_score_gemma":0.00665907,"domain_scores_codex":[0.937717,0.05773801,0.001360121,0.001471235,0.001162891,0.0005508057],"domain_scores_gemma":[0.5182161,0.4627562,0.006516569,0.008218365,0.00355476,0.0007379316],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003676737,0.001382858,0.06895325,0.00134728,0.00245824,0.007746855,0.002003954,0.6859788,0.0005820079,0.1251723,0.00460686,0.09609089],"study_design_scores_gemma":[0.0004162977,0.000594268,0.003850436,0.0002864419,0.000509645,0.002006138,0.0004036949,0.8977833,0.0002676459,0.09226613,0.001532065,0.00008395837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2490446,0.005358946,0.7346126,0.00550894,0.0001534997,0.0005331444,0.0005714398,0.0001850707,0.004031722],"genre_scores_gemma":[0.8117233,0.001568973,0.1838814,0.0003750868,0.0001095814,0.0005472914,0.0002746236,0.00005176748,0.00146813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9138381,"threshold_uncertainty_score":0.4556729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3271651649050576,"score_gpt":0.5196535078981341,"score_spread":0.1924883429930765,"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."}}