{"id":"W2028981493","doi":"10.1016/s0378-7796(99)00058-9","title":"High-speed transmission line relaying using artificial neural networks","year":2000,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Relay; Fault (geology); Transmission line; Transient (computer programming); Line (geometry); Electric power transmission; Electronic engineering; Transmission (telecommunications); Feedforward neural network; Digital protective relay; Engineering; Emtp; Computer science; Protective relay; Feed forward; Electrical engineering; Artificial intelligence; Control engineering; Electric power system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002167271,0.0003554639,0.000501747,0.0008159885,0.0004728536,0.0003020023,0.0004287781,0.0004225278,0.0002393089],"category_scores_gemma":[0.0000417675,0.0003525139,0.0001286878,0.002828768,0.00004327886,0.0003507603,0.00002405176,0.001504671,0.0002090985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007090827,"about_ca_system_score_gemma":0.00006654863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000825462,"about_ca_topic_score_gemma":0.000009229798,"domain_scores_codex":[0.9951674,0.0006924753,0.000845109,0.0005477775,0.001207643,0.00153959],"domain_scores_gemma":[0.9986132,0.0002140535,0.00005126694,0.0005821458,0.0002050154,0.0003343475],"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":[0.0001959631,0.00007128462,0.00003481006,0.0001602112,0.000112522,0.0001487153,0.0003191869,0.798584,0.1026223,0.0001075956,0.001942903,0.09570058],"study_design_scores_gemma":[0.0003345084,0.0002085633,0.00003826039,0.0001332595,0.00001121605,0.0001918417,0.00004297067,0.9782575,0.004916294,0.00002433514,0.01548689,0.0003543091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8083864,0.009037312,0.1734627,0.00004569745,0.003170354,0.001315165,0.000005145097,0.001106325,0.00347087],"genre_scores_gemma":[0.9979637,0.0001212492,0.00006129655,0.000006214083,0.000789876,0.00004026254,0.00000835644,0.000146047,0.0008629921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1895773,"threshold_uncertainty_score":0.9998927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04046502954662237,"score_gpt":0.3070035859578155,"score_spread":0.2665385564111931,"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."}}