{"id":"W4383466009","doi":"10.1177/09544070231185609","title":"Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Alberta","funders":"","keywords":"Fuel efficiency; Powertrain; Automotive engineering; Artificial neural network; Fleet management; Brake specific fuel consumption; Computer science; Electronic control unit; Engineering; Artificial intelligence; Torque; Telecommunications","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":[0.0003429948,0.000737922,0.0004364496,0.00144238,0.0001747143,0.0004887524,0.0004822584,0.0004513487,0.0003966907],"category_scores_gemma":[0.001322365,0.0001604266,0.0003241233,0.0008136515,0.000139307,0.0006484051,0.000224095,0.0002579098,0.0002461883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003947295,"about_ca_system_score_gemma":0.0003566769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01273397,"about_ca_topic_score_gemma":0.01049126,"domain_scores_codex":[0.99968,0.00005421038,0.00001811493,0.0000799949,0.0001177795,0.00004995185],"domain_scores_gemma":[0.9995983,0.000132187,0.00006002839,0.00004795959,0.0001457271,0.00001571627],"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":[0.0003494568,0.0003649221,0.1151829,0.0001523053,0.0001038167,0.0002423115,0.0001004745,0.5985937,0.01715762,0.0005187546,0.001112789,0.266121],"study_design_scores_gemma":[0.000006180316,0.00006273761,0.02897194,0.000008450963,0.00001494371,0.00005275459,0.00003238976,0.9644271,0.005840122,0.0001872518,0.0003819867,0.00001421879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093504,0.000153003,0.08712498,0.00003424533,0.0000385907,0.00003538223,0.0004637253,0.0006717305,0.002128052],"genre_scores_gemma":[0.9924378,0.00003683376,0.006591522,0.000006116455,0.000003381698,0.00001362166,0.0005184589,0.0000118685,0.0003804961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01273397,"threshold_uncertainty_score":0.0253197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019704437874042,"score_gpt":0.2495807528220832,"score_spread":0.2293837084433428,"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."}}