{"id":"W4366978163","doi":"10.1016/j.energy.2023.127592","title":"A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Greenhouse gas; Sizing; Scheduling (production processes); Schedule; Electricity; Energy consumption; Battery (electricity); Total cost; Automotive engineering; Engineering; Computer science; Power (physics); Operations management; Electrical engineering; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001707492,0.001722346,0.002247646,0.001112919,0.0007273015,0.001903733,0.001936276,0.002317111,0.009705395],"category_scores_gemma":[0.002952513,0.00100556,0.001880314,0.002414165,0.0007254667,0.001278539,0.001735025,0.001373283,0.0008513242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002509093,"about_ca_system_score_gemma":0.002993859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01742109,"about_ca_topic_score_gemma":0.01598885,"domain_scores_codex":[0.9992237,0.0003199492,0.00002596196,0.0001056962,0.0001686379,0.0001560151],"domain_scores_gemma":[0.9995166,0.0002532644,0.00003867038,0.00004239668,0.0001029607,0.0000461041],"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":[0.00002397809,0.00002299404,0.0001008318,0.00003330251,0.00001580636,0.00002840853,0.000007182398,0.9900266,0.0001333722,0.003978897,0.0007605194,0.004868164],"study_design_scores_gemma":[0.00001183109,0.0000123828,0.00008576398,0.000004356176,0.000008435653,0.000008698086,0.000005988059,0.9972983,0.00006482948,0.002048621,0.0004477268,0.000003009179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02373069,0.0005474677,0.9470665,0.0006505148,0.0001600597,0.0003337825,0.001043601,0.0005923479,0.02587507],"genre_scores_gemma":[0.7103538,0.0006998007,0.268732,0.0003258701,0.0002041903,0.000555362,0.001273314,0.0004855108,0.01737019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01742109,"threshold_uncertainty_score":0.03463942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008634862504797378,"score_gpt":0.2125056303509348,"score_spread":0.2038707678461374,"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."}}