{"id":"W2978036655","doi":"10.1016/j.trd.2019.09.020","title":"Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Greenhouse gas; Microsimulation; Traffic flow (computer networking); Transport engineering; Traffic congestion; Electrification; Computer science; Automotive engineering; Environmental science; Engineering; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002148854,0.0001349094,0.0001766753,0.0001165743,0.0001453636,0.000009004631,0.00005296959,0.00008337747,0.0004673546],"category_scores_gemma":[0.000001591529,0.0001269978,0.00003691947,0.0001208821,0.00009597133,0.0001306198,0.000002003164,0.000242993,0.00001602408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002243422,"about_ca_system_score_gemma":0.00001271755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001720709,"about_ca_topic_score_gemma":0.00002459169,"domain_scores_codex":[0.9988607,0.00001814911,0.000304216,0.0002019489,0.0003258029,0.0002892187],"domain_scores_gemma":[0.9995189,0.00008262508,0.00002542273,0.0001537286,0.00001394196,0.000205324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003317831,0.0001678511,0.5642095,0.0003254566,0.00004000708,0.00001715895,0.001872723,0.3643308,0.06560469,0.00009016248,0.00007587295,0.003232689],"study_design_scores_gemma":[0.0005864287,0.0001414727,0.9261318,0.0001751309,0.00002184929,6.95246e-7,0.0002134923,0.06883892,0.002584807,0.000006613146,0.001153864,0.0001449249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990321,0.0001032997,0.00001344271,0.00004429648,0.0000313363,0.0003738228,0.00008149173,0.0001307941,0.0001894344],"genre_scores_gemma":[0.9988838,0.0007356267,0.00008433708,0.000005103358,0.00001207818,0.00004196268,0.0001346564,0.00002506347,0.0000773229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3619224,"threshold_uncertainty_score":0.5178822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302816725912848,"score_gpt":0.2955877370933237,"score_spread":0.2725595698341952,"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."}}