{"id":"W2987488488","doi":"10.1016/j.energy.2019.116476","title":"Intelligent energy management system for conventional autonomous vehicles","year":2019,"lang":"en","type":"article","venue":"Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Australian Research Council; Australian Government; Australian Education International, Australian Government","keywords":"Automotive engineering; Energy management; Throttle; Energy consumption; Fuel efficiency; Engineering; Energy management system; Fuzzy logic; Torque; Intelligent control; Control engineering; Energy (signal processing); Computer science; Electrical engineering; Artificial intelligence","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":[0.00004184802,0.0001291407,0.0001379733,0.00007397366,0.00003437917,0.00002514797,0.0001355279,0.00006897902,0.0001053885],"category_scores_gemma":[5.372673e-7,0.0001248753,0.00008967281,0.00008983551,0.000007206426,0.00004801545,0.00002377402,0.00004119223,0.00002576403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362959,"about_ca_system_score_gemma":0.000008135165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002259105,"about_ca_topic_score_gemma":0.000004967771,"domain_scores_codex":[0.9993079,0.000007796517,0.0001833447,0.0001589585,0.0001067205,0.0002353069],"domain_scores_gemma":[0.9997194,0.00001926686,0.00002501582,0.0001621966,0.00002313404,0.00005091778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001430547,0.00001343465,0.00007856687,0.0002420076,0.0001975468,0.000005293873,0.00001895783,0.07544978,0.002178381,0.7769642,0.009648052,0.1351895],"study_design_scores_gemma":[0.0004229289,0.00006173824,0.0002548309,0.00004399925,0.00002035898,0.00001302567,0.00005576055,0.1787978,0.03327826,0.001520713,0.7852938,0.0002367178],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1013812,0.004616779,0.7848577,0.0001370059,0.005104915,0.0004606108,0.00005702553,0.001633846,0.1017509],"genre_scores_gemma":[0.9932579,0.00007295096,0.001389761,0.00008033355,0.0001779783,0.00005977436,0.00004489974,0.00003584106,0.004880515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8918768,"threshold_uncertainty_score":0.5092269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004363663084260547,"score_gpt":0.1788656526509919,"score_spread":0.1745019895667314,"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."}}