{"id":"W2714094180","doi":"10.1049/iet-gtd.2017.0346","title":"Solution techniques for transient stability‐constrained optimal power flow – Part II","year":2017,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Carleton University","funders":"Office of Science; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Transient (computer programming); Power flow; Stability (learning theory); Control theory (sociology); Flow (mathematics); Computer science; Electric power system; Power (physics); Transient flow; Mathematical optimization; Mathematics; Mechanics; Engineering; Electrical engineering; Physics; Thermodynamics; Surge; Artificial intelligence","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":[0.0006261658,0.0002673593,0.0002681559,0.00004059104,0.001213277,0.0001948672,0.0002022493,0.0002486001,0.000327134],"category_scores_gemma":[0.00006949929,0.0002647672,0.0002028558,0.00007076433,0.00009825905,0.0005122006,0.00001218166,0.0001277559,0.000004415696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002040753,"about_ca_system_score_gemma":0.00006893439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004365258,"about_ca_topic_score_gemma":0.00001294112,"domain_scores_codex":[0.9983477,0.00006687024,0.0005792968,0.0003957185,0.0002638748,0.0003465425],"domain_scores_gemma":[0.9988925,0.00001977451,0.0001025678,0.0005410549,0.0002609465,0.000183119],"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":[0.0005013246,0.001072098,0.00023345,0.00075899,0.000229837,0.000004606686,0.003147987,0.1212498,0.5985447,0.004644639,0.08385763,0.185755],"study_design_scores_gemma":[0.0007605847,0.0001507015,0.0002938566,0.00004365289,0.00003513569,0.000003585221,0.00003342213,0.6623444,0.1841092,0.00003871333,0.151848,0.0003387631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02539875,0.0000977973,0.9692401,0.001027926,0.0008216148,0.001022624,0.001109578,0.0005413081,0.0007403597],"genre_scores_gemma":[0.983888,0.00005654483,0.01296007,0.00002097624,0.0001241272,0.0002594258,0.002581013,0.0000283395,0.00008156063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9584892,"threshold_uncertainty_score":0.9999804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02454715692454391,"score_gpt":0.2481372858117357,"score_spread":0.2235901288871918,"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."}}