{"id":"W2344531617","doi":"10.1109/tpwrs.2015.2505185","title":"Computation of Maximum Loading Points via the Factored Load Flow","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Escuela Superior Politécnica del Litoral; McGill University","keywords":"Computation; Robustness (evolution); Power flow; Continuation; Electric power system; Benchmark (surveying); Mathematical optimization; Exploit; Computer science; Bisection method; Maximum flow problem; Algorithm; Flow (mathematics); Power (physics); Control theory (sociology); Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009483981,0.001335746,0.0008462492,0.001606991,0.0006253839,0.001002687,0.0007984209,0.0007594964,0.008547114],"category_scores_gemma":[0.004318285,0.0007304623,0.0006326356,0.0005879544,0.000721733,0.001917031,0.001053326,0.001198462,0.00148546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005386809,"about_ca_system_score_gemma":0.0008514865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175088,"about_ca_topic_score_gemma":0.001353374,"domain_scores_codex":[0.9996972,0.00009957758,0.00001588127,0.00004526152,0.0001141656,0.00002801916],"domain_scores_gemma":[0.9991729,0.0005181536,0.00009224591,0.00006359916,0.0001295666,0.00002351668],"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":[0.0001641986,0.00005508657,0.0006526314,0.0001915364,0.00002905265,0.0001832563,0.0002271144,0.720888,0.01018394,0.03349482,0.001959041,0.2319712],"study_design_scores_gemma":[0.00002202608,0.00004209052,0.0001138577,0.00002395338,0.000005635463,0.00003436605,0.00001784595,0.9759228,0.003618567,0.01835753,0.00182575,0.00001551585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006775604,0.00003469999,0.990655,0.00002550587,0.000008906265,0.00004668922,0.00003420321,0.0004194099,0.001999898],"genre_scores_gemma":[0.2934313,0.0001531873,0.7027925,0.0000217001,0.00002544051,0.0002623371,0.0001557361,0.0002964884,0.002861358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008547114,"threshold_uncertainty_score":0.028593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212028926557255,"score_gpt":0.2273582647763592,"score_spread":0.2061553721206337,"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."}}