{"id":"W4406703718","doi":"10.2139/ssrn.5107275","title":"Optimizing Urban Motorized Passenger Transportation Modes to Reduce Carbon Emissions","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Greenhouse gas; Environmental science; Transport engineering; Carbon fibers; Passenger transport; Sustainable transport; Environmental economics; Business; Natural resource economics; Computer science; Engineering; Economics; Sustainability; 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":[0.0001650618,0.0005443359,0.0002898503,0.0003357359,0.0002630925,0.0006426503,0.0002940477,0.0003386932,0.004199728],"category_scores_gemma":[0.0004025263,0.0001902629,0.0002851809,0.0005353443,0.0002049987,0.0003827581,0.0002637219,0.0002127303,0.0003915402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006384377,"about_ca_system_score_gemma":0.0007920971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009931073,"about_ca_topic_score_gemma":0.02734595,"domain_scores_codex":[0.9998833,0.00003953903,0.000002520088,0.00002120629,0.00001520881,0.0000382439],"domain_scores_gemma":[0.9998882,0.00004119431,0.00001680141,0.00001023586,0.00003096268,0.00001262957],"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":[0.0004349104,0.0002451911,0.009862674,0.00008036575,0.00008303197,0.00006510402,0.00005733319,0.9246612,0.01941887,0.003067391,0.001053452,0.04097034],"study_design_scores_gemma":[0.0000420545,0.0004118059,0.01495086,0.00001186319,0.00007320089,0.00002246203,0.0003964242,0.9669028,0.01011528,0.004963636,0.002091233,0.0000183456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.926239,0.0002000626,0.05852801,0.0001500552,0.0000259461,0.00005328019,0.0003387044,0.0002283212,0.01423672],"genre_scores_gemma":[0.9934689,0.00005518097,0.004181866,0.000008098506,0.000002340326,0.00001382834,0.00007395122,0.00002219791,0.002173534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009931073,"threshold_uncertainty_score":0.01974654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009172483890206293,"score_gpt":0.243314034276548,"score_spread":0.2341415503863418,"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."}}