{"id":"W3181886993","doi":"10.1002/eng2.12438","title":"A hybrid intelligent busbar protection strategy using hyperbolic S‐transforms and extreme learning machines","year":2021,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Busbar; Inrush current; Current transformer; Electronic engineering; Electric power system; Engineering; Transformer; Electric power transmission; Power-system protection; Differential protection; Fault (geology); Control theory (sociology); Computer science; Electrical engineering; Power (physics); Artificial intelligence","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":[0.0005002284,0.0006952815,0.0007229329,0.0005763189,0.000331533,0.0007152154,0.0008239814,0.0007103928,0.001036699],"category_scores_gemma":[0.0006314429,0.0002221784,0.0004678917,0.0003246255,0.0004232387,0.0006797651,0.0005369508,0.0004861356,0.0002255069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003686417,"about_ca_system_score_gemma":0.0002646811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417075,"about_ca_topic_score_gemma":0.000851829,"domain_scores_codex":[0.9996703,0.00007882997,0.00002822491,0.00009033643,0.0000893235,0.0000429547],"domain_scores_gemma":[0.9997178,0.00007701797,0.0000667577,0.0000237668,0.00009230916,0.00002233588],"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":[0.0004318908,0.0002236538,0.002248377,0.0001859782,0.0001589842,0.000394082,0.0002363582,0.670792,0.03821233,0.006630095,0.001147272,0.2793389],"study_design_scores_gemma":[0.00001139201,0.0001097463,0.0002705026,0.000005024796,0.00001381806,0.00002762858,0.000008713472,0.9960334,0.002577155,0.0006922437,0.0002445354,0.000005935479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08621255,0.000258238,0.909366,0.0001419392,0.00004553961,0.00007125208,0.00001635113,0.0005211872,0.003366825],"genre_scores_gemma":[0.9628549,0.00005315198,0.03544138,0.00006067935,0.00001529758,0.00004555861,0.00002298385,0.00001059154,0.001495461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001417075,"threshold_uncertainty_score":0.003468096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789278263609009,"score_gpt":0.20742477961148,"score_spread":0.1895319969753899,"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."}}