{"id":"W1539371187","doi":"10.1109/sai.2015.7237166","title":"A fast noise resilient anomaly detection using GMM-based collective labelling","year":2015,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"","keywords":"Constant false alarm rate; Computer science; Anomaly detection; Mixture model; Noise (video); Pattern recognition (psychology); Probabilistic logic; Labelling; Artificial intelligence; Anomaly (physics); Similarity (geometry)","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":[0.001417195,0.001109194,0.001754425,0.002261406,0.0008914626,0.001141432,0.002418709,0.001345788,0.0007745359],"category_scores_gemma":[0.00471545,0.0004172563,0.001263609,0.001998177,0.0008757589,0.001927153,0.002019541,0.001614247,0.0008932634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007630051,"about_ca_system_score_gemma":0.001153741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004559755,"about_ca_topic_score_gemma":0.004590864,"domain_scores_codex":[0.9981937,0.0002718914,0.00009216315,0.0005357974,0.0007344094,0.0001720177],"domain_scores_gemma":[0.9977076,0.0005630152,0.0002681036,0.0004753681,0.0008794712,0.0001064689],"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":[0.000317174,0.0001752931,0.009063236,0.0001599012,0.0002157012,0.0002582507,0.0004358586,0.08040442,0.05040799,0.005843019,0.004887694,0.8478314],"study_design_scores_gemma":[0.00001633682,0.0001311639,0.003037275,0.00001463586,0.00005066154,0.0004103701,0.00009948663,0.9609455,0.02357103,0.00751988,0.004124944,0.00007868682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01475006,0.0001453912,0.9827626,0.00009372524,0.00006451176,0.00003577488,0.00005702587,0.001686129,0.0004046857],"genre_scores_gemma":[0.3081866,0.0002130552,0.6882945,0.0001466747,0.0001035581,0.0001275911,0.0005233629,0.0002637878,0.002140909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004559755,"threshold_uncertainty_score":0.009066403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04146478250218805,"score_gpt":0.2736132519836677,"score_spread":0.2321484694814797,"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."}}