{"id":"W2777661585","doi":"10.1016/j.apenergy.2017.11.074","title":"Resource implications of alternative strategies for achieving zero greenhouse gas emissions from light-duty vehicles by 2060","year":2017,"lang":"en","type":"article","venue":"Applied Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Greenhouse gas; Renewable energy; Truck; Alternative fuel vehicle; Fossil fuel; Green vehicle; Biofuel; Miles per gallon gasoline equivalent; Environmental science; Electricity; Renewable fuels; Automotive engineering; Sustainability; Diesel fuel; Alternative fuels; Waste management; Engineering; Fuel efficiency","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003699206,0.0001735539,0.0002150741,0.00003976813,0.0002970993,0.00008462105,0.0004658354,0.000119488,0.00001532406],"category_scores_gemma":[0.000009756111,0.0001609484,0.00006463972,0.00005392744,0.00004546329,0.0001239697,0.00006056688,0.000120288,0.000001241654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003063066,"about_ca_system_score_gemma":0.00002094094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003672289,"about_ca_topic_score_gemma":0.0000681237,"domain_scores_codex":[0.9992074,0.000006312398,0.000228504,0.0002145954,0.00009593275,0.0002472064],"domain_scores_gemma":[0.999141,0.00008503075,0.0001282282,0.0005303246,0.00003027923,0.00008514059],"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":[0.0000268094,0.00004516442,0.000116371,0.00002734597,0.0001619944,4.637314e-7,0.0003232965,0.01130433,0.7575949,0.07797244,0.05482322,0.09760367],"study_design_scores_gemma":[0.001382586,0.00009356334,0.003551976,0.00006517631,0.00009600702,0.00000212168,0.0003139341,0.01022483,0.5650491,0.1531466,0.2653986,0.0006755405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.836973,0.0009069323,0.1161185,0.0007070737,0.0001263936,0.0002784068,0.0004520643,0.0003671389,0.04407052],"genre_scores_gemma":[0.9976302,0.0001381676,0.001721368,0.0000530052,0.0001502065,0.00007296833,0.00007625168,0.00004912569,0.0001087033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2105754,"threshold_uncertainty_score":0.6563284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008830081002446758,"score_gpt":0.2206660591889642,"score_spread":0.2118359781865175,"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."}}