{"id":"W4396937610","doi":"10.1088/2634-4505/ad397e","title":"Are vehicle lifespan caps an effective and efficient method for reducing US light-duty vehicle fleet GHG emissions?","year":2024,"lang":"en","type":"article","venue":"Environmental Research Infrastructure and Sustainability","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Saudi Aramco; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Greenhouse gas; Life-cycle assessment; Software deployment; Occupancy; Automotive engineering; Electric vehicle; Work (physics); Market penetration; Environmental economics; Environmental science; Engineering; Production (economics); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001344566,0.0003551203,0.0003579833,0.0002216325,0.0005849011,0.0002536458,0.0001984523,0.0002965186,0.00007326999],"category_scores_gemma":[0.000319274,0.0002984143,0.00008955693,0.0003542623,0.0002809264,0.0002809043,0.0002200803,0.001075409,0.00000184847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009498515,"about_ca_system_score_gemma":0.00006879304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000454311,"about_ca_topic_score_gemma":0.000006600965,"domain_scores_codex":[0.9971478,0.0003427196,0.0003186831,0.000827598,0.0004605336,0.0009026767],"domain_scores_gemma":[0.9984545,0.000515581,0.00003511686,0.0004291854,0.00005748492,0.00050819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003505793,0.0001807635,0.03992115,0.002902882,0.000191915,0.0001138115,0.005849625,0.03158024,0.1000955,0.0005497122,0.003333295,0.8149305],"study_design_scores_gemma":[0.0008121051,0.0005379937,0.54366,0.000094027,0.0000452945,0.00005600875,0.004304382,0.3968732,0.02394619,0.01605532,0.01308247,0.0005330656],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99226,0.00346389,0.001940391,0.0003921743,0.0001316438,0.001448713,0.00009835234,0.0001682807,0.00009652172],"genre_scores_gemma":[0.997946,0.0001130764,0.001316339,0.00004481916,0.0002377531,0.0001695683,0.00002091226,0.00006890279,0.00008259582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8143975,"threshold_uncertainty_score":0.9999468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007475446528370366,"score_gpt":0.2972106771724046,"score_spread":0.2897352306440342,"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."}}