{"id":"W2125428223","doi":"10.1109/59.852154","title":"Contingency screening for steady-state security analysis by using FFT and artificial neural networks","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fast Fourier transform; Artificial neural network; Computer science; Contingency table; Preprocessor; Artificial intelligence; Machine learning; Data mining; Reliability engineering; Algorithm; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803982,0.000793784,0.0005050983,0.001134644,0.0003583316,0.0003779611,0.0003728923,0.0003640845,0.00213848],"category_scores_gemma":[0.001855831,0.0002606296,0.0003046819,0.0007817869,0.000258854,0.00123655,0.0003607021,0.0004572965,0.0003144188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003512929,"about_ca_system_score_gemma":0.0003395443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00294206,"about_ca_topic_score_gemma":0.004383263,"domain_scores_codex":[0.9997223,0.00007607371,0.00002377981,0.00003989455,0.0001188588,0.00001907043],"domain_scores_gemma":[0.9992811,0.0004096575,0.00008670022,0.00005218537,0.0001517023,0.00001871463],"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":[0.0002394415,0.0001219494,0.006020337,0.00018017,0.00009327361,0.0003721751,0.00008762487,0.2615836,0.03114617,0.00701231,0.002431011,0.6907119],"study_design_scores_gemma":[0.000005927599,0.00004721549,0.001913814,0.00001000833,0.00001225841,0.00008296231,0.0000168236,0.9913622,0.00394534,0.0020047,0.0005881492,0.00001069405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03218609,0.000168401,0.964615,0.00006264591,0.00003681292,0.00004545186,0.00006678647,0.00110825,0.001710525],"genre_scores_gemma":[0.5204979,0.0002392637,0.4773306,0.00003295373,0.00004575491,0.0001175608,0.0001695471,0.00006992037,0.001496597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00294206,"threshold_uncertainty_score":0.007153869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539544410440372,"score_gpt":0.2337566001704485,"score_spread":0.2183611560660448,"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."}}