{"id":"W4293160534","doi":"10.1007/978-3-031-01497-0","title":"Smart Charging and Anti-Idling Systems","year":2018,"lang":"en","type":"book","venue":"Synthesis lectures on advances in automotive technology","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Energy conservation; Automotive engineering; Reduction (mathematics); Efficient energy use; Service (business); Environmental economics; Business; Engineering; Environmental science; Electrical engineering; Marketing; Economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002249249,0.0005320128,0.0007860391,0.001218642,0.0001238422,0.00003533941,0.0003852754,0.0008908998,0.00006395287],"category_scores_gemma":[0.000235508,0.0004856627,0.00005891433,0.0002953279,0.000333851,0.0001287375,0.00008794933,0.001034224,0.0000653286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003147433,"about_ca_system_score_gemma":0.00004929167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001730272,"about_ca_topic_score_gemma":0.00001734244,"domain_scores_codex":[0.9982836,0.00003584252,0.000404656,0.0005606238,0.0001837099,0.0005316154],"domain_scores_gemma":[0.9989184,0.0003619182,0.0001301526,0.0004736409,0.00005632421,0.00005957281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001143208,0.0001728193,0.00327563,0.006524416,0.0007202966,0.0004607012,0.0008420492,0.09381536,0.004685004,0.03083172,0.01565685,0.8429008],"study_design_scores_gemma":[0.0003936258,0.0002099625,0.0002904644,0.007687315,0.00006644348,0.000135761,0.00009696752,0.03490358,0.02356754,0.009193674,0.9217949,0.001659744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04369005,0.2028618,0.004766873,0.0004846002,0.004766742,0.002102621,0.0002300181,0.006926468,0.7341708],"genre_scores_gemma":[0.9450026,0.03148469,0.00160073,0.00009536852,0.0006554119,0.0003243806,0.00002056016,0.0003859003,0.02043033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9061381,"threshold_uncertainty_score":0.9997595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005643942759507655,"score_gpt":0.2247359888116051,"score_spread":0.2190920460520975,"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."}}