{"id":"W2128602454","doi":"10.1142/s0218213014600215","title":"A Mixture Model-Based Combination Approach for Outlier Detection","year":2014,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Computer science; Anomaly detection; Pattern recognition (psychology); Artificial intelligence; Data mining; Identification (biology); Local outlier factor","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006809536,0.000113494,0.0001469851,0.0002762858,0.0001104299,0.0003198163,0.0009925101,0.00008863307,0.000006793965],"category_scores_gemma":[0.0002453898,0.0001048006,0.0001930289,0.000201169,0.00004434073,0.0006073545,0.00004492256,0.000172469,0.000006985729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000674,"about_ca_system_score_gemma":0.000071783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003131335,"about_ca_topic_score_gemma":0.000002218416,"domain_scores_codex":[0.9986359,0.0000416739,0.0005755926,0.0002011474,0.0004095799,0.0001361216],"domain_scores_gemma":[0.9979498,0.0001558019,0.0004517847,0.0002010603,0.001171982,0.00006952597],"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":[0.00006088118,0.0002297045,0.000005629273,0.000004714349,0.00002498654,5.304569e-7,0.0001036473,0.05616777,0.009717348,0.2002229,0.0001185822,0.7333432],"study_design_scores_gemma":[0.00005491007,0.0001768445,0.00001320124,0.000008852054,0.000006887336,0.00001430796,0.00001958459,0.7606593,0.1563855,0.08115213,0.001419315,0.00008909983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001764881,0.000008653069,0.9957086,0.001318793,0.0004569509,0.0002134934,0.000005031243,0.00006690373,0.0004566658],"genre_scores_gemma":[0.7503142,0.00000339248,0.2490762,0.0002909575,0.0002311668,0.00003714759,0.000003887775,0.000007642018,0.00003543245],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7485493,"threshold_uncertainty_score":0.4273645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04797987691904263,"score_gpt":0.3131231068887541,"score_spread":0.2651432299697114,"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."}}