{"id":"W3162142206","doi":"10.1021/acs.est.0c06671","title":"Greenhouse Gas Emission Mitigation Pathways for Urban Passenger Land Transport under Ambitious Climate Targets","year":2021,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; National University of Singapore","keywords":"Greenhouse gas; Electrification; Climate change mitigation; Climate change; Public transport; Business; Natural resource economics; Modal shift; Environmental science; Software deployment; Electricity; Environmental planning; Environmental economics; Transport engineering; Engineering; Economics","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":[0.0001295052,0.0001341412,0.0001231401,0.0001334413,0.000310243,0.00001503932,0.0001826907,0.0001408576,0.0001228428],"category_scores_gemma":[0.000006790361,0.0001289882,0.00004033396,0.0003616661,0.0003059836,0.0002047469,0.00004865014,0.0001521715,0.00002638069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466727,"about_ca_system_score_gemma":0.00002269946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001884318,"about_ca_topic_score_gemma":0.000009062184,"domain_scores_codex":[0.9989073,0.000004464629,0.0001733778,0.0003031034,0.000179313,0.0004324329],"domain_scores_gemma":[0.9996102,0.00001131206,0.00002725359,0.0002454005,0.000007033682,0.00009877187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005003581,0.00006482181,0.02556308,0.0000224803,0.000005205456,0.0000186463,0.0001255658,0.002466685,0.9583353,0.0002347632,0.0002122785,0.01294615],"study_design_scores_gemma":[0.0006792549,0.0001298607,0.03070358,0.00004757867,0.00001614923,0.00007110277,0.0003815414,0.02551083,0.9196489,0.001599888,0.02081147,0.0003998454],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996194,0.0003120324,0.001995482,0.0004062958,0.0001527343,0.0001479872,0.00005135491,0.0002931667,0.0004469299],"genre_scores_gemma":[0.9977264,0.0004614604,0.001541277,0.00006315546,0.00003436999,0.00004030314,0.00003625336,0.00002203089,0.00007468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03868642,"threshold_uncertainty_score":0.5259988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005687065776309824,"score_gpt":0.1917339061836449,"score_spread":0.1860468404073351,"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."}}