{"id":"W4385976946","doi":"10.1016/j.est.2023.108565","title":"Multi-period planning of locations and capacities of public charging stations","year":2023,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Charging station; Beijing; Randomness; Queueing theory; Electric vehicle; Computer science; Operations research; Simulation; Environmental science; Engineering; Geography; Mathematics; Statistics; Power (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001190095,0.00005886971,0.0001491715,0.0002984674,0.00003798342,0.00001141544,0.00006561604,0.00003520168,0.00001253547],"category_scores_gemma":[0.00003461194,0.00005377799,0.00003579902,0.000273764,0.00003888968,0.0001704269,0.000008862829,0.0001021847,1.314676e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002323073,"about_ca_system_score_gemma":0.00003177872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001203171,"about_ca_topic_score_gemma":0.000006088991,"domain_scores_codex":[0.9994707,0.00001446576,0.0002682226,0.00003367966,0.0001079966,0.0001049507],"domain_scores_gemma":[0.9996268,0.00004071555,0.0001274619,0.00005562311,0.0001045596,0.00004482135],"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":[0.000007452768,0.00003171803,0.004717513,0.0002110493,0.0003133739,0.0000339435,0.007987275,0.825759,0.139899,0.006718799,0.003068589,0.0112523],"study_design_scores_gemma":[0.002709305,0.0005398229,0.1351501,0.0006774429,0.0001533542,0.0003962585,0.01978035,0.7646549,0.04861937,0.002072222,0.02454568,0.0007012588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9601994,0.001521132,0.03776138,0.0001018541,0.000143446,0.00001285406,0.00001504898,0.00002268948,0.0002222177],"genre_scores_gemma":[0.9975412,0.000225514,0.002089597,0.000007522147,0.00005137822,7.879974e-7,0.00000353545,0.00001157659,0.00006890867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1304326,"threshold_uncertainty_score":0.2193003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003338175824065,"score_gpt":0.2301695498378462,"score_spread":0.2101361680796056,"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."}}