{"id":"W2990393024","doi":"10.1103/physrevd.101.042003","title":"Machine-learning nonstationary noise out of gravitational-wave detectors","year":2020,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Association of Canadian Universities for Research in Astronomy; National Aeronautics and Space Administration; California Institute of Technology; Space Telescope Science Institute; Massachusetts Institute of Technology; National Science Foundation","keywords":"LIGO; Noise (video); Gravitational wave; Detector; Physics; SIGNAL (programming language); Coupling (piping); Noise measurement; Gaussian noise; Noise floor; Algorithm; Acoustics; Computer science; Noise reduction; Optics; Artificial intelligence; Engineering; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001708663,0.00112815,0.001248118,0.0007249117,0.0006105938,0.001189305,0.001523068,0.001228289,0.000936505],"category_scores_gemma":[0.005989038,0.000505157,0.0008164094,0.0007622056,0.0008276003,0.00130082,0.001168259,0.001741108,0.0005929829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000649345,"about_ca_system_score_gemma":0.001321054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126711,"about_ca_topic_score_gemma":0.002596695,"domain_scores_codex":[0.999386,0.0001623539,0.00003849567,0.0002161246,0.0001353562,0.00006167204],"domain_scores_gemma":[0.9978276,0.001498817,0.0002222278,0.0001653513,0.0002239904,0.00006198748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002683214,0.0001538387,0.004970911,0.0002709272,0.0001919292,0.0001792806,0.0001817828,0.5419853,0.01340943,0.0298867,0.003363127,0.4051385],"study_design_scores_gemma":[0.00001035157,0.00002534948,0.0004177673,0.000008634987,0.00001042333,0.00002384513,0.000008150889,0.9840204,0.002150284,0.01254145,0.0007765844,0.000006853205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02511498,0.0004525607,0.9728296,0.0002539221,0.00004580977,0.00003092667,0.00006407019,0.0004611527,0.0007469934],"genre_scores_gemma":[0.5150008,0.0007829795,0.4754896,0.0004190218,0.0002948982,0.0002296143,0.001422883,0.000249546,0.006110698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002126711,"threshold_uncertainty_score":0.009036362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02774693283669182,"score_gpt":0.4438380154404361,"score_spread":0.4160910826037442,"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."}}