{"id":"W2167249129","doi":"10.1109/wescan.1993.270582","title":"A multi-input power system stabilizer based on artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Control theory (sociology); Computer science; Electric power system; Generator (circuit theory); Memorization; Power (physics); Error function; Control engineering; Backpropagation; Stabilizer (aeronautics); Artificial intelligence; Engineering; Control (management); Algorithm; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001680054,0.0001937941,0.0002152852,0.00007187318,0.00006381712,0.0000640849,0.0001154395,0.0001119224,0.001655527],"category_scores_gemma":[0.00002428787,0.0001685826,0.00009570048,0.0002092722,0.00002098495,0.00007777138,0.00001077497,0.0001467335,0.0001597182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001262923,"about_ca_system_score_gemma":0.000003232868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004856592,"about_ca_topic_score_gemma":0.00001944391,"domain_scores_codex":[0.9988553,0.00007527685,0.0003474372,0.0002335371,0.0001825511,0.0003058896],"domain_scores_gemma":[0.9993224,0.00006848393,0.00002401951,0.0004093357,0.00004612162,0.0001296652],"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":[0.000008375733,0.00006762664,0.0004386753,0.00003828255,0.000007846277,0.000005192922,0.0001001332,0.9977171,0.00004531957,0.0001422802,0.001164862,0.0002643487],"study_design_scores_gemma":[0.0003128513,0.00003067917,0.0003843011,0.00001524289,0.000004587742,0.000001920221,0.0001049735,0.9982323,0.00006343105,1.823861e-7,0.0006591627,0.0001903894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01844085,0.00008877165,0.9329895,0.0001061169,0.001908683,0.0004839827,0.00001353513,0.001825884,0.04414272],"genre_scores_gemma":[0.9983121,6.560861e-7,0.001255389,0.0001180503,0.00003408897,0.00002552881,0.000004524659,0.00003593602,0.0002137335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9798712,"threshold_uncertainty_score":0.9992571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061277990270304,"score_gpt":0.2068820584868167,"score_spread":0.1862692785841136,"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."}}