{"id":"W2902415114","doi":"10.1137/1.9781611975673.66","title":"LSCP: Locally Selective Combination in Parallel Outlier Ensembles","year":2019,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"PricewaterhouseCoopers (Canada); University of Toronto","funders":"","keywords":"Outlier; Linear subspace; Anomaly detection; Computer science; Base (topology); Task (project management); Stability (learning theory); Artificial intelligence; Pattern recognition (psychology); Data mining; Local outlier factor; Feature (linguistics); Machine learning; Mathematics; Engineering","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":[0.0003321155,0.0003336391,0.0004838174,0.00007636698,0.0001730724,0.0001256677,0.0003730584,0.0007717228,0.000004331727],"category_scores_gemma":[0.000006646912,0.0003228876,0.0002886981,0.00004001062,0.00009804989,0.00004928902,0.0001866857,0.000510933,0.00001287959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074024,"about_ca_system_score_gemma":0.0001735172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003316976,"about_ca_topic_score_gemma":0.000004148687,"domain_scores_codex":[0.9985196,0.000003617933,0.0004919677,0.0005080214,0.0002259613,0.0002508567],"domain_scores_gemma":[0.9989048,0.0001882876,0.0003749878,0.0003765236,0.00008902953,0.00006642373],"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":[0.000007073621,0.00003138688,2.548909e-7,0.00007008681,0.00005498456,1.282875e-7,0.0005842725,0.000005182213,0.0001258075,0.9764934,0.002308247,0.02031918],"study_design_scores_gemma":[0.00165214,0.0001714989,7.676065e-7,0.0001514713,0.00006612382,0.000005059227,0.0001310304,0.004384097,0.001388559,0.9406642,0.05080504,0.0005799827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0001234182,0.00003486858,0.5951776,0.000214138,0.0001129417,0.003501685,0.00004248826,0.0002695107,0.4005233],"genre_scores_gemma":[0.02549236,0.0001248666,0.3721786,0.0006879969,0.0005886266,0.001756994,0.00009178943,0.0002243611,0.5988544],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2229989,"threshold_uncertainty_score":0.9999223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0393636122034942,"score_gpt":0.2402044409217747,"score_spread":0.2008408287182805,"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."}}