{"id":"W2972363742","doi":"10.3390/en12183485","title":"Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Noise reduction; Partial discharge; Radio frequency; Artificial neural network; Thresholding; Computer science; Artificial intelligence; Noise (video); Wavelet; Energy (signal processing); Pattern recognition (psychology); Electronic engineering; Voltage; Engineering; Mathematics; Electrical engineering; Telecommunications","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":[0.0002706859,0.0001221411,0.0002375952,0.00005698636,0.000111609,0.0000638019,0.0001600918,0.00004937034,0.00105846],"category_scores_gemma":[0.00004063753,0.0001078959,0.00006047791,0.0002366982,0.00005801762,0.0002763023,0.00004325953,0.00005861791,0.00008332903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003046178,"about_ca_system_score_gemma":0.00005971753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001350854,"about_ca_topic_score_gemma":0.000006316826,"domain_scores_codex":[0.9987514,0.00009145853,0.0003614975,0.0002180261,0.0002410973,0.000336496],"domain_scores_gemma":[0.9994325,0.00008782649,0.0001700494,0.0002056485,0.0000478802,0.0000561152],"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":[0.00001925808,0.00001746018,0.002214056,0.000008210129,0.000004136374,0.000001421605,0.0001676061,0.2057922,0.7742055,0.01743918,0.00003182947,0.00009909174],"study_design_scores_gemma":[0.0003447441,0.0001086595,0.002519764,0.00004678484,0.0000274727,0.000005872774,0.0001109662,0.03865288,0.9476221,0.009996888,0.0002074281,0.0003564933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964901,0.0003586927,0.001232433,0.00002831372,0.0008714836,0.00009940028,0.0000064466,0.00006544033,0.0008477],"genre_scores_gemma":[0.9977641,0.000003375205,0.001594547,0.00005566802,0.0004750515,0.000002958128,0.000004752305,0.00001468848,0.00008487934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1734165,"threshold_uncertainty_score":0.9998547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387679676802997,"score_gpt":0.254519164083269,"score_spread":0.230642367315239,"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."}}