{"id":"W1966333251","doi":"10.1109/tpwrs.2006.873100","title":"Oscillatory Stability Limit Prediction Using Stochastic Subspace Identification","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"Powertech Labs (Canada); University of Waterloo","funders":"","keywords":"Electric power system; Control theory (sociology); Subspace topology; Stability (learning theory); Identification (biology); Tripping; Transient (computer programming); Limit (mathematics); Computer science; System identification; Generator (circuit theory); Noise (video); Mode (computer interface); Engineering; Power (physics); Mathematics; Data modeling; Artificial intelligence; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000478477,0.0002610068,0.000269204,0.0002430197,0.0002181777,0.0001862414,0.0001237234,0.0001928685,0.0001039687],"category_scores_gemma":[0.00000510124,0.0002840452,0.0001351865,0.0004507199,0.0000558197,0.0004517919,5.540343e-7,0.0002176654,0.00005098532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006470267,"about_ca_system_score_gemma":0.00005681063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001467239,"about_ca_topic_score_gemma":0.00005948498,"domain_scores_codex":[0.9980568,0.0001502239,0.0006921362,0.0004002452,0.0003990072,0.0003015603],"domain_scores_gemma":[0.9990259,0.0001094624,0.00006768024,0.00055612,0.0001371908,0.0001036139],"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":[0.00001101677,0.00007378877,0.00006216008,0.0001444786,0.00003647153,0.00000102874,0.0002373191,0.9931633,0.005998204,0.00006287316,0.0001801599,0.0000291319],"study_design_scores_gemma":[0.0002498444,0.00003138871,0.0006541148,0.00009947489,0.0000416469,0.00002351357,0.000225104,0.9941853,0.003397465,0.000005794931,0.0008008657,0.0002854297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06099096,0.0003903383,0.9281687,0.000008728537,0.007540794,0.000651293,0.0002363128,0.001155831,0.0008570761],"genre_scores_gemma":[0.9995277,0.000005716131,0.00008742173,0.000002922104,0.0000227924,0.0000824229,0.00001242903,0.00005559034,0.0002029962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9385368,"threshold_uncertainty_score":0.9999612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629664622567792,"score_gpt":0.2049794255429705,"score_spread":0.1886827793172926,"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."}}