{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001999281,0.00009254881,0.0001138609,0.0000411342,0.00002960707,0.00005046966,0.00007292944,0.00003885893,0.004086192],"category_scores_gemma":[0.00006001378,0.00007952017,0.00001833458,0.0001460005,0.00001841941,0.0001212872,0.000034898,0.00004663229,0.0001589704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003642031,"about_ca_system_score_gemma":0.00000573556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005918433,"about_ca_topic_score_gemma":0.00003642422,"domain_scores_codex":[0.9994059,0.00001971819,0.0001700106,0.0001548556,0.00008255384,0.0001669136],"domain_scores_gemma":[0.9995423,0.00003905422,0.000006304128,0.0002321535,0.00003708065,0.0001430874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006710814,0.0006866858,0.3934919,0.002369361,0.0001424374,0.000008786451,0.008927661,0.06699029,0.01321943,0.008651257,0.4382044,0.06724067],"study_design_scores_gemma":[0.0004214996,0.00004749827,0.3319802,0.00001641236,0.000004634854,0.00000421399,0.0003967468,0.6280233,0.000656226,0.0001765266,0.03779884,0.0004738925],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3901926,0.0000566313,0.3383206,0.0001606003,0.0002437682,0.000565065,0.000006995121,0.0004257155,0.2700281],"genre_scores_gemma":[0.996773,0.000003516016,0.001544639,0.00008496497,0.00000595826,0.00003327824,9.228788e-7,0.000009352202,0.001544336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6065804,"threshold_uncertainty_score":0.9968242,"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."}}