{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001614782,0.0008546456,0.001195935,0.001263904,0.0007139872,0.00176548,0.001439097,0.001274997,0.007507692],"category_scores_gemma":[0.003452772,0.001599005,0.001006057,0.001822544,0.0005679677,0.001762497,0.0006886254,0.0008830426,0.0004384867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498018,"about_ca_system_score_gemma":0.002210154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01834543,"about_ca_topic_score_gemma":0.03227881,"domain_scores_codex":[0.9993469,0.0002710427,0.00002349753,0.0001103516,0.00006059408,0.0001875697],"domain_scores_gemma":[0.9981328,0.001123864,0.0002486209,0.00006583853,0.000181855,0.0002472183],"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":[0.0001220535,0.00002207374,0.0006410464,0.00003416781,0.00003481922,0.00006417557,0.0000245865,0.9933624,0.0002806618,0.001502422,0.0003812521,0.003530355],"study_design_scores_gemma":[0.00002960792,0.00008504872,0.001300928,0.00001392023,0.00003455352,0.00002328833,0.00009979273,0.9952122,0.0003589435,0.002272096,0.0005535701,0.00001592161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6601838,0.001131156,0.3042606,0.001050616,0.0001983728,0.0005585572,0.003890006,0.0005622961,0.02816463],"genre_scores_gemma":[0.9826393,0.0001471066,0.01359887,0.0000186069,0.00001110208,0.0001007772,0.0003050713,0.00002744898,0.003151726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01834543,"threshold_uncertainty_score":0.03647733,"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."}}