{"id":"W4307741923","doi":"10.1115/1.4056105","title":"A Model-Free Kullback–Leibler Divergence Filter for Anomaly Detection in Noisy Data Series","year":2022,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kullback–Leibler divergence; Divergence (linguistics); Series (stratigraphy); Anomaly detection; Anomaly (physics); Filter (signal processing); Mathematics; Computer science; Pattern recognition (psychology); Artificial intelligence; Statistics; Geology; Physics; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002742973,0.0008471948,0.001384057,0.0009750814,0.0004973161,0.001420536,0.001363993,0.001371391,0.00103613],"category_scores_gemma":[0.007077987,0.000341936,0.0007970963,0.0008443668,0.0009745572,0.001582071,0.00117944,0.001633158,0.0005838044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008691,"about_ca_system_score_gemma":0.001816137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00426422,"about_ca_topic_score_gemma":0.003069444,"domain_scores_codex":[0.9984243,0.0002976276,0.0001193788,0.0003727607,0.0006823537,0.0001036099],"domain_scores_gemma":[0.9975548,0.001278416,0.0002406342,0.0001879497,0.0006378772,0.0001002792],"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":[0.0003838999,0.0002324356,0.002782104,0.0002839616,0.000171981,0.0002147576,0.0001507305,0.4209949,0.03017831,0.01962144,0.004066735,0.5209188],"study_design_scores_gemma":[0.000008092494,0.00005147001,0.0003696445,0.000007201993,0.000007838825,0.00005643997,0.000007813478,0.9925478,0.003223769,0.002930263,0.0007741093,0.00001564534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005755293,0.0001315809,0.9934347,0.00009497108,0.00002992188,0.0000140926,0.00003189392,0.0003115859,0.0001959965],"genre_scores_gemma":[0.3964117,0.0004169956,0.5987163,0.0003366133,0.0001530622,0.0001506834,0.000585434,0.0001743741,0.003054788],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00426422,"threshold_uncertainty_score":0.0145064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04424346354661939,"score_gpt":0.2448868912196054,"score_spread":0.200643427672986,"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."}}