{"id":"W2120478495","doi":"10.1109/epec.2013.6802945","title":"Accommodating high penetration of PEV in distribution networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Penetration (warfare); Sorting; Mathematical optimization; Computer science; Integer programming; Distributed generation; Linear programming; Genetic algorithm; Nonlinear system; Distributed computing; Operations research; Engineering; Algorithm; Mathematics; Renewable energy; Electrical engineering","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":[0.0004473911,0.000682433,0.0005353017,0.0004128426,0.0004705083,0.0007346579,0.0007148004,0.0006508231,0.001263039],"category_scores_gemma":[0.0009235647,0.0004223178,0.0003723229,0.0005133536,0.000284455,0.0008187867,0.0005822709,0.0005903415,0.0001615313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006219189,"about_ca_system_score_gemma":0.0007146823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005041854,"about_ca_topic_score_gemma":0.006810782,"domain_scores_codex":[0.9997751,0.00007352458,0.000007088543,0.0000419103,0.00005272545,0.00004977346],"domain_scores_gemma":[0.9997135,0.000155364,0.00004468343,0.00001583826,0.00004645686,0.00002416237],"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":[0.00001442868,0.0000132046,0.0004039003,0.00001701223,0.000008462428,0.0000540989,0.00001816531,0.9789389,0.001115805,0.001513597,0.0001963331,0.01770598],"study_design_scores_gemma":[0.00000426716,0.00002129078,0.0001215445,0.000003484638,0.000004630549,0.00002243052,0.00001569298,0.996949,0.0007697027,0.001524845,0.0005599826,0.00000312287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0610367,0.000156109,0.9334071,0.0001402375,0.00002032531,0.00005789115,0.0000540249,0.000240691,0.004886887],"genre_scores_gemma":[0.8535728,0.0001795283,0.1427732,0.00006044441,0.00001413621,0.00008623141,0.00009625591,0.00005414973,0.003163242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005041854,"threshold_uncertainty_score":0.01002502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00255536742830381,"score_gpt":0.163813605096234,"score_spread":0.1612582376679302,"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."}}