{"id":"W2601314166","doi":"10.1109/tpwrs.2017.2754940","title":"A Second-Order Cone Programming Model for Planning PEV Fast-Charging Stations","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Second-order cone programming; Computer science; Order (exchange); Electric power system; Mathematical optimization; Power (physics); Mathematics; Physics; Economics","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":[0.001694407,0.001922668,0.001809859,0.0007069891,0.000566392,0.001873994,0.001969301,0.001561263,0.003858108],"category_scores_gemma":[0.002320577,0.001219634,0.00141734,0.001444659,0.0009028982,0.001320392,0.0009929725,0.002550978,0.0004842221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780969,"about_ca_system_score_gemma":0.002790688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02101556,"about_ca_topic_score_gemma":0.01691486,"domain_scores_codex":[0.9991633,0.0003246294,0.00002999831,0.0001428628,0.0001986728,0.0001405345],"domain_scores_gemma":[0.9987625,0.0007221172,0.0001458169,0.00004097294,0.0002169179,0.000111707],"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":[0.000009037535,0.00001104414,0.00008148898,0.00001869067,0.000006100365,0.00003210888,0.00000919838,0.9922504,0.00008827781,0.005981247,0.0003352791,0.001177219],"study_design_scores_gemma":[0.000003082823,0.000008068831,0.0000162861,0.000002264012,0.000001498278,0.000003673992,0.000004158052,0.9981736,0.00002263469,0.001610913,0.0001518179,0.000001994254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01109253,0.0003419221,0.9817754,0.0003100232,0.00006419332,0.00008583243,0.0003515059,0.0001434332,0.005835303],"genre_scores_gemma":[0.7100281,0.001136824,0.2714102,0.0002858841,0.0001172781,0.0007690841,0.001139163,0.0001775472,0.0149359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02101556,"threshold_uncertainty_score":0.04178643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01738245918397806,"score_gpt":0.251398292818309,"score_spread":0.2340158336343309,"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."}}