{"id":"W3134661225","doi":"10.1007/s40042-021-00094-2","title":"Time series anomaly detection for gravitational-wave detectors based on the Hilbert–Huang transform","year":2021,"lang":"en","type":"article","venue":"Journal of the Korean Physical Society","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"LIGO; Waveform; Series (stratigraphy); Event (particle physics); Generator (circuit theory); A priori and a posteriori; Anomaly detection; Cluster analysis; Anomaly (physics); Computer science; Detector; Algorithm; Physics; Data mining; Pattern recognition (psychology); Artificial intelligence; Astrophysics; Radar; Geology; Power (physics); Telecommunications; Quantum mechanics","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":[0.0006697576,0.0003599758,0.0004795712,0.0006927139,0.0002290479,0.0005264357,0.000469091,0.000402809,0.001095448],"category_scores_gemma":[0.001848166,0.0001406422,0.0003125735,0.0005979186,0.0003832331,0.0009818503,0.0004469255,0.0005759938,0.0002979516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002183911,"about_ca_system_score_gemma":0.0005687618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007774353,"about_ca_topic_score_gemma":0.0007055431,"domain_scores_codex":[0.9996241,0.0000959574,0.00002188402,0.00008041585,0.0001420786,0.00003552683],"domain_scores_gemma":[0.9992987,0.0003552658,0.00006808011,0.00005752916,0.00017638,0.00004408697],"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":[0.0006969309,0.000361443,0.01208548,0.0001842087,0.0001983696,0.000294301,0.0001714443,0.06155189,0.2087774,0.04596413,0.003605962,0.6661083],"study_design_scores_gemma":[0.000009791916,0.00007535105,0.003025657,0.000003569232,0.00001952649,0.0001774384,0.00001689908,0.9759926,0.0136099,0.00622793,0.0008211535,0.00002014163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1044273,0.0003125425,0.8930122,0.0001839234,0.00007087708,0.00001828316,0.0001041312,0.000532906,0.001337902],"genre_scores_gemma":[0.858713,0.0002807926,0.1388028,0.00006373797,0.0001269565,0.00003414174,0.0003366036,0.00004752019,0.001594412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001095448,"threshold_uncertainty_score":0.003664613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193608158860137,"score_gpt":0.275282911521771,"score_spread":0.2633468299331697,"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."}}