{"id":"W2598585128","doi":"10.4271/2017-01-0425","title":"A Global Optimal Energy Management System for Hybrid Electric off-road Vehicles","year":2017,"lang":"en","type":"article","venue":"SAE International journal of commercial vehicles","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive engineering; Energy management; Energy (signal processing); Computer science; Engineering; Physics","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":[0.0002877524,0.000226269,0.0003505734,0.0002586627,0.000241875,0.0002894391,0.001700972,0.00008463144,0.000004911128],"category_scores_gemma":[0.00005706474,0.0002180036,0.0002574347,0.00008209501,0.00007512504,0.0003837564,0.0001523253,0.0001857099,0.000005987114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004195791,"about_ca_system_score_gemma":0.00004710602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004525064,"about_ca_topic_score_gemma":0.00002737762,"domain_scores_codex":[0.9983872,0.00002249855,0.0005772351,0.0001560451,0.0005069737,0.0003500413],"domain_scores_gemma":[0.9987999,0.00006075225,0.000384137,0.0002797656,0.0003872679,0.0000881274],"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":[0.0002390643,0.00006695725,0.003058326,0.00004681688,0.0007232853,0.0002228358,0.00001190326,0.001105691,0.0008130267,0.01425057,0.00854504,0.9709165],"study_design_scores_gemma":[0.01141004,0.001327518,0.6183341,0.001275288,0.0007360808,0.00270966,0.0003900996,0.09018634,0.0803672,0.01309285,0.1782458,0.001925068],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759938,0.002548953,0.01540782,0.001148728,0.001777101,0.0001490156,0.00005944683,0.0002609134,0.002654196],"genre_scores_gemma":[0.9960653,0.001143228,0.001862768,0.00007125539,0.0007538109,0.00001759125,0.000005323568,0.00003038214,0.00005034091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9689914,"threshold_uncertainty_score":0.8889931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119778597346162,"score_gpt":0.2561095858597996,"score_spread":0.2441317261251834,"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."}}