{"id":"W3006008512","doi":"10.1155/2020/4169826","title":"Stochastic Electric Vehicle Network with Elastic Demand and Environmental Costs","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Scientific Research Foundation of the Graduate School of Southeast University; National Key Research and Development Program of China; Natural Science Research of Jiangsu Higher Education Institutions of China; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; Nantong Science and Technology Bureau; Science and Technology Project of Nantong City; Southeast University; National Natural Science Foundation of China","keywords":"Electric vehicle; Transport engineering; Computer science; Flow network; Operations research; Range (aeronautics); Engineering; Mathematical optimization; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001175632,0.00007203847,0.0001263365,0.00003599231,0.0001637886,0.00001828987,0.0000468646,0.00003715192,0.00001597394],"category_scores_gemma":[0.0000185862,0.00006503902,0.00002522305,0.0002094516,0.00004779035,0.0003699037,2.846905e-7,0.000119748,8.723059e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003235565,"about_ca_system_score_gemma":0.00005777391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006415896,"about_ca_topic_score_gemma":0.00009071674,"domain_scores_codex":[0.9992182,0.00003387456,0.0002431306,0.0000934828,0.0002778099,0.0001334948],"domain_scores_gemma":[0.9994676,0.00007679061,0.0002413433,0.00002175001,0.00004510415,0.0001473901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004404316,0.00002205021,0.01790586,0.000008796503,0.00002353751,0.00002035387,0.01100954,0.9663779,0.001005019,0.000369308,0.00002178546,0.002795486],"study_design_scores_gemma":[0.003923663,0.001386678,0.9768023,0.000191513,0.0003391903,0.000009119451,0.01007435,0.00525779,0.0001727431,0.0003613127,0.001114044,0.0003672731],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8958588,0.0004207957,0.1030257,0.0004544178,0.00006859949,0.0001135456,0.000005506907,0.00001776067,0.0000348466],"genre_scores_gemma":[0.9964894,0.000212401,0.002999363,0.0001198211,0.0001383017,0.000001477521,0.00002239866,0.00000901436,0.000007800237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.96112,"threshold_uncertainty_score":0.2652214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005783966125409159,"score_gpt":0.2214304993104641,"score_spread":0.2156465331850549,"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."}}