{"id":"W1892057141","doi":"10.1109/ccece.1993.332292","title":"An artificial neural network based directional discriminator for protecting transmission lines","year":2002,"lang":"en","type":"article","venue":"","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discriminator; Artificial neural network; Computer science; Artificial intelligence; Electric power transmission; Boundary (topology); Feedforward neural network; Pattern recognition (psychology); Transmission (telecommunications); Feed forward; Machine learning; Engineering; Control engineering; Mathematics; Electrical engineering; Telecommunications; Detector","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":[],"consensus_categories":[],"category_scores_codex":[0.0001799403,0.0001452483,0.0001293741,0.00006215709,0.0002063567,0.00005161067,0.00007008619,0.00009176219,0.0001251672],"category_scores_gemma":[0.00002187461,0.000132727,0.00007836655,0.0001619137,0.000009458937,0.000194471,0.000002251759,0.0001336579,0.000009416348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000460498,"about_ca_system_score_gemma":0.000003417692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000168291,"about_ca_topic_score_gemma":0.0000421455,"domain_scores_codex":[0.9991363,0.00004068109,0.0002388764,0.0001889186,0.0001248107,0.0002704429],"domain_scores_gemma":[0.9996598,0.00006486248,0.00002331375,0.0001278505,0.00003550748,0.00008863259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007671991,0.0001049775,0.0001367684,0.0002745592,0.00003074144,0.000002896174,0.0003936258,0.3660637,0.1936373,0.00008001502,0.003053295,0.4361453],"study_design_scores_gemma":[0.0001720603,0.00009153214,0.00009303953,0.00002528332,0.00001000376,0.000005947984,0.00003035475,0.9437808,0.03895325,0.00006112008,0.01660306,0.0001735184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1110346,0.000112925,0.883754,0.000109379,0.001963852,0.0007264336,0.000006125827,0.001385215,0.000907489],"genre_scores_gemma":[0.9922957,6.71246e-7,0.00617922,0.00002079219,0.001115569,0.000201337,0.000007053989,0.00004582153,0.0001338758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8812611,"threshold_uncertainty_score":0.5412451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02879604064015685,"score_gpt":0.2462905218273676,"score_spread":0.2174944811872107,"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."}}