{"id":"W2887380491","doi":"10.23977/isspj.2017.21003","title":"Multi-objective Planning Model of Electric Vehicle Charging Station","year":2017,"lang":"en","type":"article","venue":"Information Systems and Signal Processing Journal","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Charging station; Electric vehicle; Genetic algorithm; Operator (biology); Point (geometry); Location model; Service (business); Tabu search; Computer science; Scale (ratio); Simulation; Operations research; Mathematical optimization; Engineering; Algorithm; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007654123,0.001172061,0.001027931,0.000736058,0.0006148386,0.001625632,0.002018989,0.001382882,0.007248455],"category_scores_gemma":[0.0008715912,0.000720366,0.001054605,0.001481258,0.0005927133,0.001020239,0.0007817217,0.00109154,0.0005537936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756878,"about_ca_system_score_gemma":0.001754569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03217513,"about_ca_topic_score_gemma":0.0167768,"domain_scores_codex":[0.9995203,0.0001364195,0.00001911892,0.0001054786,0.0001222916,0.00009648136],"domain_scores_gemma":[0.999702,0.0001247971,0.000048605,0.00001053165,0.00008399327,0.00002998313],"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":[0.000008833793,0.000006160903,0.00009010141,0.0000252089,0.000007859306,0.00004805024,0.00001491904,0.9952246,0.0001295305,0.002599038,0.0002281494,0.00161746],"study_design_scores_gemma":[0.000006199878,0.000009264019,0.00005946008,0.000003184215,0.000005059083,0.000006881015,0.000009698198,0.9984983,0.00005186892,0.001076104,0.000270532,0.00000348656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04448349,0.001059404,0.9182241,0.000767924,0.000114346,0.0002055997,0.0009407348,0.0003512531,0.03385315],"genre_scores_gemma":[0.8987131,0.001281079,0.0728245,0.0001242447,0.00005492234,0.0006745138,0.0007496527,0.00007760744,0.02550029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03217513,"threshold_uncertainty_score":0.06397569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102017074351119,"score_gpt":0.2550861826850889,"score_spread":0.2340660119415777,"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."}}