{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003431967,0.0003581871,0.0003250922,0.000599367,0.000171063,0.0003567427,0.000203704,0.0002217684,0.0005263113],"category_scores_gemma":[0.002272449,0.0001299925,0.0001753168,0.0002991143,0.0002654623,0.0005339498,0.0003777148,0.0003632319,0.0001581696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002125884,"about_ca_system_score_gemma":0.0003122453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001072054,"about_ca_topic_score_gemma":0.001124145,"domain_scores_codex":[0.999793,0.00005302234,0.00001300142,0.0000366737,0.00008802473,0.00001627218],"domain_scores_gemma":[0.9992163,0.0003644532,0.0001530117,0.00006649121,0.0001735595,0.00002610342],"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":[0.0002152741,0.0001030038,0.006887569,0.00006423634,0.00002799152,0.00008206846,0.0001073721,0.7699767,0.02945374,0.006828247,0.0008782132,0.1853757],"study_design_scores_gemma":[0.000001733107,0.00001092742,0.0004326212,0.000001497231,8.757016e-7,0.00000912308,0.000003545181,0.9963768,0.002284694,0.0008005725,0.00007438003,0.000003279739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1207818,0.00005487983,0.8766178,0.00005624356,0.0000113505,0.00001948904,0.00004521642,0.0006513292,0.00176172],"genre_scores_gemma":[0.9497804,0.00004212398,0.04968787,0.000009271554,0.000007325455,0.00002715882,0.00008105065,0.00001825386,0.0003465087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001072054,"threshold_uncertainty_score":0.002131641,"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."}}