{"id":"W2754314498","doi":"10.1002/tee.22519","title":"Fault location in an unbalanced distribution system using support vector classification and regression analysis","year":2017,"lang":"en","type":"article","venue":"IEEJ Transactions on Electrical and Electronic Engineering","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kementerian Pendidikan; Universiti Malaya","keywords":"Support vector machine; Fault (geology); Ranking (information retrieval); Fault indicator; Kernel (algebra); Data mining; Generalization; Pattern recognition (psychology); Computer science; Engineering; Artificial intelligence; Algorithm; Fault detection and isolation; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002151417,0.0002211299,0.0002887801,0.0003452185,0.000259759,0.000135958,0.0001143447,0.0001802351,0.000001957169],"category_scores_gemma":[0.00001502979,0.0002263533,0.00004966785,0.000610447,0.00001953678,0.0004481452,0.000002232742,0.0004040491,0.000001610416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007916889,"about_ca_system_score_gemma":0.00002915851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008781304,"about_ca_topic_score_gemma":0.0001436539,"domain_scores_codex":[0.9987299,0.00003109071,0.0002978232,0.0003313916,0.0001719457,0.0004378103],"domain_scores_gemma":[0.9994268,0.00003014589,0.00006531591,0.0003199288,0.00004308575,0.000114716],"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":[0.0001593913,0.0001507175,0.003083697,0.0004782657,0.0005034642,0.00001289927,0.0002535964,0.7465009,0.1460822,0.001354028,0.000007027119,0.1014138],"study_design_scores_gemma":[0.0003508052,0.0001454535,0.0281803,0.00006462698,0.0001329776,0.00002782112,0.00001962177,0.9665336,0.004204289,0.000006164667,0.0001035604,0.0002308007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.470149,0.0001909664,0.5291379,0.00001564884,0.000122598,0.0001453472,0.000007461389,0.000216632,0.00001449389],"genre_scores_gemma":[0.9996371,0.000118293,0.00008184308,0.000001634055,0.00004453892,0.00004984007,0.00002715543,0.00002877118,0.00001086986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5294881,"threshold_uncertainty_score":0.9230418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026671209377801,"score_gpt":0.2436763045009979,"score_spread":0.2334095924072199,"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."}}