{"id":"W182693664","doi":"10.1007/978-3-642-30353-1_32","title":"Bayesian Multiple Imputation Approaches for One-Class Classification","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Bayesian probability; Class (philosophy); Computer science; Artificial intelligence; Machine learning; Missing data","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007019013,0.0003653281,0.0003301006,0.0005691748,0.0003835489,0.0003642056,0.001735302,0.0003627624,0.000007662462],"category_scores_gemma":[0.00005430298,0.0003692193,0.00014852,0.0004691994,0.0003256968,0.0006693231,0.0003925766,0.0004042971,0.00002388751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000321193,"about_ca_system_score_gemma":0.0002327572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000916311,"about_ca_topic_score_gemma":0.00002872236,"domain_scores_codex":[0.9973077,0.00002074611,0.0004822839,0.00118927,0.0004914065,0.0005086415],"domain_scores_gemma":[0.9977384,0.000382974,0.0003854198,0.001120816,0.0002163581,0.0001560507],"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":[0.000002991711,0.00003947109,0.0000314173,0.00002796359,0.000006114392,3.323206e-7,0.0001983835,0.002723841,0.0002527311,0.1109209,0.00001768225,0.8857781],"study_design_scores_gemma":[0.0001408869,0.00008323602,0.0002463946,0.00005377285,0.000009416467,0.00001060712,2.473266e-7,0.8216946,0.002591425,0.1708664,0.003901514,0.0004014766],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000132503,0.0001468596,0.9952281,0.001093176,0.0004008006,0.00109207,0.00001012526,0.0003592825,0.001656356],"genre_scores_gemma":[0.4133708,0.00002025871,0.5855733,0.0002822693,0.000367278,0.0001628376,0.00001987749,0.0000278445,0.0001755193],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8853766,"threshold_uncertainty_score":0.999876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05957947064101748,"score_gpt":0.2655907012758431,"score_spread":0.2060112306348256,"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."}}