{"id":"W2768324790","doi":"10.1049/hve.2017.0119","title":"Denoising different types of acoustic partial discharge signals using power spectral subtraction","year":2017,"lang":"en","type":"article","venue":"High Voltage","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Noise reduction; White noise; Noise measurement; Noise (video); Acoustics; Computer science; Partial discharge; Noise spectral density; Wavelet; Additive white Gaussian noise; Speech recognition; Mathematics; Artificial intelligence; Engineering; Physics; Telecommunications; Electrical engineering; Noise figure; Bandwidth (computing); Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002728399,0.0001843733,0.0003206784,0.00008930979,0.0004758578,0.000194564,0.0002970489,0.00008105436,0.001852386],"category_scores_gemma":[0.0001886756,0.0001527191,0.00007852307,0.00005873824,0.0001277213,0.0004668561,0.00007328329,0.0001087947,0.0001283459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007676135,"about_ca_system_score_gemma":0.00004027658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002484035,"about_ca_topic_score_gemma":0.00002501088,"domain_scores_codex":[0.9985964,0.00005080181,0.0003837072,0.0002983751,0.0003069258,0.0003638422],"domain_scores_gemma":[0.9988315,0.00006581119,0.0004535646,0.0004742227,0.00006147363,0.0001133932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000597386,0.0001039888,0.002797785,0.00001820428,0.000008314209,0.000006769405,0.0002052857,0.0003918182,0.9948272,0.001465089,0.00005177502,0.00006400132],"study_design_scores_gemma":[0.00058688,0.0001002587,0.08540861,0.00004794278,0.0000640616,0.000005775985,0.00003293062,0.003699403,0.9083228,0.001252449,0.0001933593,0.0002855862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666694,0.00007519561,0.03142052,0.00003152858,0.0009066523,0.0001699426,0.00002787563,0.00005472805,0.0006441686],"genre_scores_gemma":[0.9991499,0.000009410338,0.0002270298,0.00002769807,0.0002523566,0.000004668366,0.000005985592,0.00002084108,0.000302164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08650448,"threshold_uncertainty_score":0.99906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559107886740804,"score_gpt":0.2789027298960973,"score_spread":0.2533116510286892,"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."}}