{"id":"W2130400949","doi":"10.1109/tpwrs.2006.873010","title":"Evenly Distributed Pareto Points in Multi-Objective Optimal Power Flow","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Power flow; Pareto principle; Mathematical optimization; Intersection (aeronautics); Multi-objective optimization; Electric power system; Pareto analysis; Pareto optimal; Flow (mathematics); Power (physics); Computer science; Mathematics; Engineering; Geometry; Physics","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":[0.003002017,0.0008650701,0.001335206,0.001862794,0.001218923,0.002049394,0.001078347,0.001123026,0.002669954],"category_scores_gemma":[0.006388732,0.0006066845,0.0008040633,0.00165278,0.001485173,0.002675193,0.002241877,0.001580473,0.0007055672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124379,"about_ca_system_score_gemma":0.0008859158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001320014,"about_ca_topic_score_gemma":0.001045381,"domain_scores_codex":[0.9978462,0.0008634291,0.00005529824,0.0001749335,0.0008738923,0.0001861811],"domain_scores_gemma":[0.9988469,0.0006080118,0.00005589551,0.0001041948,0.0003328556,0.00005218991],"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":[0.0001958754,0.00009441767,0.0005393012,0.00009504701,0.00002989726,0.00009030661,0.0002550612,0.7585936,0.003747863,0.109076,0.001341135,0.1259415],"study_design_scores_gemma":[0.00001854337,0.00004499858,0.0001246229,0.00001527776,0.000005456017,0.00001818497,0.0000598785,0.9467189,0.002146928,0.04926361,0.001571084,0.0000125313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02284904,0.0001842221,0.9701588,0.00008504323,0.00002179665,0.00003936293,0.00001706307,0.0001240993,0.006520534],"genre_scores_gemma":[0.6203375,0.0003627426,0.3731094,0.00007566007,0.00004701866,0.0003394033,0.0001237723,0.000234535,0.005369889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003002017,"threshold_uncertainty_score":0.01587635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008737540782902741,"score_gpt":0.2153952483940129,"score_spread":0.2066577076111102,"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."}}