{"id":"W3039869690","doi":"10.1016/j.rser.2020.110012","title":"Well-to-wheel greenhouse gas implications of mid-level ethanol blend deployment in Canada's light-duty fleet","year":2020,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gasoline; Greenhouse gas; Cellulosic ethanol; Environmental science; Ethanol fuel; Biofuel; Fossil fuel; Fuel efficiency; Software deployment; Waste management; Environmental engineering; Engineering; Automotive engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004837421,0.0002487437,0.0001479343,0.0006091116,0.002032598,0.001892382,0.0006363156,0.000769756,0.003287414],"category_scores_gemma":[0.0006857747,0.0001254379,0.0003991591,0.0009738439,0.0007693619,0.0007466122,0.0006517311,0.0009731361,0.000180569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03759224,"about_ca_system_score_gemma":0.0449773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9902879,"about_ca_topic_score_gemma":0.9975938,"domain_scores_codex":[0.9994738,0.00003257857,0.000007574235,0.00003065282,0.0001724659,0.0002829146],"domain_scores_gemma":[0.9992723,0.00005505573,0.00004629848,0.000009104026,0.0004845942,0.0001326834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002131817,0.0008241753,0.3114542,0.001476941,0.0004151908,0.002463511,0.005867451,0.05611942,0.0380762,0.06863162,0.1355347,0.3770048],"study_design_scores_gemma":[0.00004659473,0.0003452928,0.6790196,0.0009581995,0.0003115603,0.0001477258,0.03016756,0.009671125,0.01463056,0.005400856,0.2590752,0.0002256082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8530619,0.01198982,0.001492802,0.01946482,0.0003576698,0.00009207353,0.003217268,0.00008885882,0.1102348],"genre_scores_gemma":[0.9584228,0.008109098,0.0006060486,0.001748187,0.00001592558,0.0000131153,0.0006722134,0.00002165148,0.03039108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03759224,"threshold_uncertainty_score":0.272752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02203027064538103,"score_gpt":0.233008522092762,"score_spread":0.210978251447381,"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."}}