{"id":"W4285006084","doi":"10.22215/etd/2022-15103","title":"Environmental Impacts of Connected and Automated Vehicles Considering Traffic Flow and Road Characteristics","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Greenhouse gas; Crash; Transport engineering; Traffic flow (computer networking); Environmental science; Acceleration; Traffic congestion; Automotive engineering; Reduction (mathematics); Computer science; Engineering","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":[0.0001480512,0.0004907643,0.0002768254,0.0004122509,0.0003702147,0.0007497933,0.0003747284,0.0005645983,0.001415019],"category_scores_gemma":[0.0004170298,0.000190048,0.0006849637,0.0004389259,0.0003157152,0.0005817697,0.00034235,0.0002892306,0.0001325868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007870136,"about_ca_system_score_gemma":0.0005338094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01973363,"about_ca_topic_score_gemma":0.02172568,"domain_scores_codex":[0.9998842,0.00002966071,0.000003017808,0.00002230685,0.00002831776,0.00003241422],"domain_scores_gemma":[0.9997719,0.0001342095,0.00002122697,0.00001353178,0.00004318178,0.00001603504],"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":[0.00008291432,0.0001053216,0.008017349,0.00002938959,0.00004108701,0.0001154791,0.00003466905,0.9833804,0.003238113,0.0005925611,0.00008309609,0.00427955],"study_design_scores_gemma":[0.00001618834,0.000316234,0.0125683,0.00001157795,0.00006237101,0.00003997097,0.0002387157,0.9818809,0.003205907,0.0009967778,0.0006408803,0.00002227514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930454,0.00004411337,0.002489229,0.00002556395,0.000009535711,0.0000155555,0.0001213567,0.00002355762,0.004225549],"genre_scores_gemma":[0.9984347,0.00005941945,0.0005917292,0.000003798362,0.000001712092,0.000006769466,0.00009376366,0.000005651426,0.0008025524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01973363,"threshold_uncertainty_score":0.03923756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003702708540441511,"score_gpt":0.186549846923208,"score_spread":0.1828471383827664,"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."}}