{"id":"W4221021081","doi":"10.1007/s00521-022-07066-y","title":"An interpretable CNN model for classification of partial discharge waveforms in 3D-printed dielectric samples with different void sizes","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial discharge; Waveform; Computer science; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Artificial neural network; Void (composites); Cluster analysis; Fourier transform; Voltage; Materials science; Mathematics; Electrical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001580081,0.00009559576,0.0001563317,0.00008587537,0.0003053091,0.00003296668,0.0001560513,0.00001729699,0.00001571038],"category_scores_gemma":[0.00001061673,0.00007485327,0.00001956633,0.0002446146,0.00003665428,0.00007865489,0.0000513107,0.00009196118,4.488813e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004066409,"about_ca_system_score_gemma":0.00002656131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000454943,"about_ca_topic_score_gemma":0.00001926379,"domain_scores_codex":[0.9991071,0.00004197067,0.0002661472,0.0002739026,0.0001232693,0.000187641],"domain_scores_gemma":[0.9994813,0.0001111638,0.000140833,0.0001751239,0.00004169987,0.00004984471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002056453,0.0005330767,0.008198754,0.00005600899,0.000007070195,1.098379e-7,0.001502326,0.08226427,0.863895,0.02649393,0.0000117342,0.01683208],"study_design_scores_gemma":[0.0003478568,0.0001517922,0.01027382,0.000004395201,0.000008165007,0.00000149918,0.00014534,0.9819135,0.006243774,0.0007564328,0.00005497142,0.00009845717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7086511,0.00001754288,0.2907615,0.0000698334,0.00001463661,0.0003659355,0.00003141602,0.00004167363,0.00004638245],"genre_scores_gemma":[0.9983981,0.000002224338,0.001099581,0.00004602697,0.00002799465,0.000328838,0.00006256964,0.000009968609,0.00002472794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8996492,"threshold_uncertainty_score":0.3052428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675802577024088,"score_gpt":0.2771683660356454,"score_spread":0.2504103402654045,"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."}}