{"id":"W2074223939","doi":"10.1049/iet-gtd.2012.0340","title":"Identification of generator loss‐of‐excitation from power‐swing conditions using a fast pattern classification method","year":2013,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Swing; Generator (circuit theory); Identification (biology); Excitation; Computer science; Power (physics); Control theory (sociology); Artificial intelligence; Electrical engineering; Engineering; Physics; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003793811,0.0002141526,0.0002934502,0.0001210757,0.0001494219,0.00007529543,0.0001286774,0.0002060584,0.0004402679],"category_scores_gemma":[0.00004466482,0.0002272992,0.0001321891,0.0004177639,0.00005020756,0.0006054511,0.000007444446,0.0001104192,0.00001775722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776683,"about_ca_system_score_gemma":0.00005931981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009563,"about_ca_topic_score_gemma":0.000006506056,"domain_scores_codex":[0.9976877,0.0002466685,0.00118511,0.0003223736,0.0003766292,0.0001815268],"domain_scores_gemma":[0.9985678,0.00006462604,0.0003372915,0.0003431597,0.0005724467,0.0001146891],"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":[0.000005063354,0.00007543842,0.0006996896,0.00006892853,0.00003598607,1.364263e-7,0.0004940055,0.07322317,0.9196789,0.0003002727,0.0004422511,0.004976124],"study_design_scores_gemma":[0.000319843,0.00001771894,0.01467871,0.00004095854,0.0000430953,0.00000137513,0.0001855507,0.7337509,0.2504718,0.0001661838,0.0001574631,0.0001663935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3660296,0.0001188647,0.6323715,0.00007102256,0.0002796056,0.0003703955,0.0006477386,0.00008709699,0.00002423544],"genre_scores_gemma":[0.9895696,0.00004070541,0.005593846,0.00001236517,0.00005302602,0.00007699472,0.004615307,0.00002744443,0.00001070039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6692071,"threshold_uncertainty_score":0.9268991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255039071561334,"score_gpt":0.2691448308190858,"score_spread":0.2465944401034725,"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."}}