{"id":"W2802576550","doi":"10.1016/j.trd.2018.04.011","title":"Development of a hybrid modelling approach for the generation of an urban on-road transportation emission inventory","year":2018,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mesoscopic physics; Microsimulation; Computer science; Sample (material); Quality (philosophy); Transport engineering; Environmental science; Engineering; Physics","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":[0.0004733476,0.000675137,0.0006868826,0.0006797697,0.0005437427,0.001101051,0.001355343,0.001136651,0.002529022],"category_scores_gemma":[0.0007504302,0.0006999549,0.001117553,0.0006785211,0.0002298111,0.001010623,0.0007796217,0.0007746388,0.0004815521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008637683,"about_ca_system_score_gemma":0.00156392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02364505,"about_ca_topic_score_gemma":0.02235552,"domain_scores_codex":[0.9998044,0.00004910789,0.000011771,0.00003875229,0.00006627438,0.00002969038],"domain_scores_gemma":[0.9997284,0.0001033699,0.00002106605,0.00003244992,0.0000952086,0.00001949943],"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":[0.00001181162,0.00002928599,0.000383701,0.00001703733,0.00001917467,0.00003311493,0.00001629924,0.9910556,0.001143549,0.00115939,0.000110724,0.006020134],"study_design_scores_gemma":[0.000002154367,0.000005379036,0.00006558941,0.000001453683,0.000003142583,0.00000296093,0.000004257912,0.999146,0.0002658386,0.0003085235,0.0001917986,0.00000293577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04984646,0.00007194748,0.9413005,0.00009657523,0.00004062568,0.0001140765,0.0003714531,0.0008179797,0.007340463],"genre_scores_gemma":[0.7769891,0.000146229,0.2163405,0.00004685751,0.00002509715,0.0002924517,0.0007244043,0.0002289703,0.005206428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02364505,"threshold_uncertainty_score":0.04701483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066223366085195,"score_gpt":0.2865696075494569,"score_spread":0.1799472709409374,"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."}}