{"id":"W4409017078","doi":"10.1016/j.scs.2025.106298","title":"Enhancing public transportation sustainability: Insights from electric bus scheduling and charge optimization","year":2025,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Sustainability; Public transport; Scheduling (production processes); Charge (physics); Automotive engineering; Transport engineering; Engineering; Computer science; Environmental economics; Economics; Operations management; Physics; Ecology","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":[0.000387065,0.000712517,0.0005437761,0.0003246803,0.0002715906,0.001098002,0.0005514213,0.0007137851,0.002360368],"category_scores_gemma":[0.001433589,0.0003010712,0.0004947164,0.0005598246,0.0005475654,0.001219521,0.0006626293,0.0006734607,0.0001601135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152899,"about_ca_system_score_gemma":0.001603461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01141541,"about_ca_topic_score_gemma":0.01013824,"domain_scores_codex":[0.9997972,0.00008762984,0.000004695149,0.00002675614,0.00005107691,0.00003269938],"domain_scores_gemma":[0.9997093,0.0001652712,0.00004867604,0.00001807602,0.00003991127,0.00001873453],"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":[0.00000722688,0.00002058967,0.0002921985,0.00001385352,0.000006827138,0.00002601634,0.00001275648,0.9783796,0.0002069586,0.01602965,0.0003264012,0.004678017],"study_design_scores_gemma":[0.000004098623,0.00001501959,0.0001897708,0.000005484381,0.000004969623,0.00001063737,0.00002197593,0.9870529,0.0001220488,0.01128202,0.0012875,0.000003549239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1426314,0.001375123,0.7868866,0.002981323,0.0001407828,0.00007757788,0.0002960621,0.0002906726,0.06532045],"genre_scores_gemma":[0.9716409,0.001028004,0.02086718,0.0001023842,0.00006217777,0.00003994456,0.00008197757,0.00007305024,0.0061044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01141541,"threshold_uncertainty_score":0.02269787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002249752657172335,"score_gpt":0.1771778756791643,"score_spread":0.1749281230219919,"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."}}