{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002086588,0.0002433073,0.0002439538,0.0001510114,0.00007411459,0.00008953769,0.00003182767,0.00008447966,0.00001856437],"category_scores_gemma":[0.00008038927,0.0002646225,0.00006414061,0.0002157446,0.000009464118,0.0002216361,0.00001956921,0.0003576901,0.000003393784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000136969,"about_ca_system_score_gemma":0.00002482798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005866384,"about_ca_topic_score_gemma":0.000007030771,"domain_scores_codex":[0.9987863,0.00001244777,0.0004178728,0.0002984493,0.0001835702,0.0003013977],"domain_scores_gemma":[0.9995657,0.00001305319,0.00005060815,0.0002131371,0.00005175158,0.0001058177],"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":[0.000001868095,0.000008387679,0.0002999374,0.0002830673,0.00006068126,0.0007021483,0.0002094501,0.6590201,0.3102744,0.000007099862,0.00000486712,0.02912799],"study_design_scores_gemma":[0.00009310316,0.00002790006,0.0006319958,0.0001816861,0.00002706716,0.009740272,0.00005377307,0.8282252,0.1530165,0.00003694564,0.007587954,0.0003776497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7262564,0.001482475,0.2702084,0.000003038681,0.001008211,0.0001746035,8.024718e-7,0.0006998028,0.0001663124],"genre_scores_gemma":[0.9988393,0.00006705459,0.0006484562,0.000001688203,0.0001981413,0.00003316641,0.000006639166,0.00008285072,0.0001226866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2725829,"threshold_uncertainty_score":0.9999806,"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."}}