{"id":"W2555983448","doi":"10.1109/pesgm.2016.7741567","title":"Queuing analysis based siting of PEV charging stations considering on distribution system impact","year":2016,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Toronto","keywords":"Queueing theory; Poisson distribution; Charging station; Electric vehicle; Computer science; Queue management system; Automotive engineering; Homogeneous; Simulation; Environmental science; Engineering; Statistics; Computer network; Mathematics; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001395605,0.001100966,0.001182755,0.001092145,0.0008683996,0.001645763,0.001884049,0.0006491681,0.003317773],"category_scores_gemma":[0.002672093,0.0006727765,0.001070498,0.001143842,0.0005526691,0.001848643,0.0008305142,0.0008797864,0.0003039956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002448187,"about_ca_system_score_gemma":0.002931375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01989339,"about_ca_topic_score_gemma":0.01933554,"domain_scores_codex":[0.9990236,0.0002459681,0.00004654058,0.0002261763,0.0002405761,0.0002171394],"domain_scores_gemma":[0.9984733,0.0008314927,0.0001735415,0.00007419824,0.000346392,0.0001009914],"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":[0.00006600038,0.0000671343,0.001829412,0.00005431944,0.00003648518,0.00006852594,0.00003956677,0.9663193,0.00260354,0.006524072,0.0004961494,0.02189545],"study_design_scores_gemma":[0.000004653596,0.00003460282,0.0003697389,0.000003753267,0.00002015349,0.00001008134,0.00002783007,0.9968615,0.0007585041,0.001621696,0.0002788189,0.000008727256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05198889,0.0002220504,0.9430577,0.0001968784,0.00009757717,0.0001111865,0.0001200973,0.0004661011,0.003739672],"genre_scores_gemma":[0.9120008,0.0003071525,0.08381201,0.00006230975,0.0000598274,0.00006904227,0.0001842311,0.00007576905,0.003428884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01989339,"threshold_uncertainty_score":0.03955525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005504773683041773,"score_gpt":0.2123309616135189,"score_spread":0.2068261879304771,"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."}}