{"id":"W2042518280","doi":"10.1109/peoco.2013.6564518","title":"Market efficiency and MW margin to voltage instability","year":2013,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity market; Electricity; Restructuring; Schedule; Electric power system; Margin (machine learning); Deregulation; Voltage; Work (physics); Computer science; Energy market; Power (physics); Electrical engineering; Economics; Engineering; Market economy; Mechanical engineering; Finance","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":[0.001002878,0.000275624,0.00024746,0.0006576224,0.0002359142,0.001127391,0.0003028279,0.0004755886,0.004801805],"category_scores_gemma":[0.009572093,0.0001836507,0.0002163895,0.000765297,0.0005845783,0.001065422,0.0005366627,0.0004493056,0.0002349106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008390965,"about_ca_system_score_gemma":0.000428548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001283143,"about_ca_topic_score_gemma":0.001227466,"domain_scores_codex":[0.9993929,0.0002943564,0.00001790196,0.00007721515,0.0001211989,0.00009654684],"domain_scores_gemma":[0.9963143,0.002762672,0.0005433417,0.00008911997,0.0002258815,0.00006459718],"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":[0.00006607894,0.00003837545,0.006763632,0.00005681642,0.00002260645,0.000209455,0.0001279389,0.7291507,0.002390921,0.2294534,0.001874037,0.02984612],"study_design_scores_gemma":[0.00001142663,0.00009735968,0.007789975,0.00002895605,0.00001365614,0.0001705159,0.0001682388,0.8871552,0.001514159,0.0993223,0.003711666,0.00001667281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3930979,0.0009612149,0.4870251,0.001260101,0.00004010264,0.00007040764,0.0002299777,0.0001471885,0.1171681],"genre_scores_gemma":[0.9915861,0.0001713643,0.005475997,0.00002022552,0.00001389711,0.00001996428,0.00003090006,0.00002130353,0.002660246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004801805,"threshold_uncertainty_score":0.01606363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005034941327708744,"score_gpt":0.1849666065245021,"score_spread":0.1799316651967934,"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."}}