{"id":"W2391884680","doi":"10.1080/15325008.2016.1148082","title":"Superimposed Energy-based Fault Detection and Classification Scheme for Series-compensated Line","year":2016,"lang":"en","type":"article","venue":"Electric Power Components and Systems","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Scheme (mathematics); Fault detection and isolation; Computer science; Line (geometry); Fault (geology); Classification scheme; Energy (signal processing); Pattern recognition (psychology); Data mining; Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics; Geology; Seismology","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":[0.0002434931,0.0002527642,0.0003165603,0.0002512599,0.0001369037,0.00009176332,0.00008020349,0.0001751894,0.000002672086],"category_scores_gemma":[0.00002590808,0.0001985,0.00004809508,0.0002429164,0.00002412372,0.0002298478,0.000009668945,0.00007638644,0.000005968862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611535,"about_ca_system_score_gemma":0.00001470438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005829136,"about_ca_topic_score_gemma":0.00001312525,"domain_scores_codex":[0.998652,0.00006204407,0.0004223884,0.0003346159,0.0001849671,0.0003439506],"domain_scores_gemma":[0.999365,0.00008716005,0.0000904369,0.0002283036,0.0001035011,0.0001256404],"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":[0.000106025,0.00001439274,0.0002078947,0.00008409445,0.00005284272,0.000001057498,0.00002661777,0.00002203726,0.9941182,0.0001339519,0.00008814989,0.005144696],"study_design_scores_gemma":[0.003781265,0.0006652834,0.008753873,0.0002230154,0.00003884538,0.0001636603,0.00003666996,0.7455104,0.145803,0.00004679695,0.0942467,0.0007304771],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7920729,0.00111525,0.2043369,0.00004896725,0.001278673,0.0005356193,0.00002207871,0.0004589857,0.0001306792],"genre_scores_gemma":[0.9993092,0.0000536393,0.00007878014,0.000009856642,0.0001156555,0.0001772642,0.0000140539,0.00005457864,0.0001869709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8483152,"threshold_uncertainty_score":0.8094596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949159342529927,"score_gpt":0.2159564181055167,"score_spread":0.1964648246802174,"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."}}