{"id":"W2735045385","doi":"10.1109/i2mtc.2017.7969684","title":"Robust detection of acoustic partial discharge signals in noisy environments","year":2017,"lang":"en","type":"article","venue":"","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Partial discharge; Robustness (evolution); Feature extraction; Frequency domain; Computer science; Pattern recognition (psychology); Artificial intelligence; Noise (video); Noise measurement; Acoustic emission; Benchmark (surveying); Speech recognition; Noise reduction; Acoustics; Engineering; Voltage; Computer vision; Physics","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":[0.0004443029,0.0005544907,0.0006128668,0.0008067042,0.0001458006,0.0005004525,0.000390476,0.0006098618,0.0003129823],"category_scores_gemma":[0.002227086,0.0001417376,0.0002507604,0.0003938732,0.0002533061,0.0004963244,0.0004694368,0.0004641283,0.0004692065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122019,"about_ca_system_score_gemma":0.0001537705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004150542,"about_ca_topic_score_gemma":0.0004257827,"domain_scores_codex":[0.9995628,0.00005938218,0.00002465929,0.0001016955,0.0002167861,0.00003474116],"domain_scores_gemma":[0.999369,0.0002456721,0.0001253635,0.00006893228,0.0001693393,0.00002182773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005534647,0.0001662291,0.009877685,0.0002799937,0.00008573361,0.0004640958,0.00009897831,0.03852301,0.4490763,0.0006011341,0.002271528,0.498002],"study_design_scores_gemma":[0.00003484766,0.0004294312,0.03276098,0.00002748311,0.00006244954,0.00129036,0.0001122134,0.6538662,0.3059342,0.001078925,0.004341678,0.00006119017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4731258,0.001257947,0.5212403,0.0001953423,0.0001315682,0.00005079856,0.0003365332,0.001896127,0.001765652],"genre_scores_gemma":[0.9225896,0.0006418764,0.07461981,0.00007010863,0.00008295995,0.00003919476,0.0007560433,0.00006079177,0.001139638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008067042,"threshold_uncertainty_score":0.002349734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867153228532511,"score_gpt":0.2479216534999927,"score_spread":0.2192501212146676,"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."}}