{"id":"W4391342051","doi":"10.1109/vppc60535.2023.10403391","title":"Reduced-Scale Hardware-in-the-Loop Platform for Dual-Source Off-Road Electric Vehicle using Energetic Macroscopic Representation","year":2023,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Hardware-in-the-loop simulation; Computer science; Reliability (semiconductor); Scale (ratio); Electric vehicle; Dual (grammatical number); Embedded system; Simulation; Automotive engineering; Engineering","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":[0.0002988569,0.0004690379,0.0003492761,0.0001845701,0.0001669999,0.0004448521,0.0008543867,0.0003017951,0.003798629],"category_scores_gemma":[0.0005202795,0.0001690221,0.0003235441,0.00008564588,0.0003074785,0.0006566453,0.0004610223,0.0004284126,0.0004453611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003294867,"about_ca_system_score_gemma":0.0004561374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605633,"about_ca_topic_score_gemma":0.001474153,"domain_scores_codex":[0.9998083,0.00004884083,0.000006275806,0.00002429967,0.00009412556,0.00001811101],"domain_scores_gemma":[0.9998116,0.00005901712,0.0000169633,0.00005692268,0.00004479961,0.00001075514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002390981,0.0001694013,0.002446292,0.000228121,0.00004240966,0.0003137983,0.0001304519,0.8454956,0.08723848,0.00956083,0.001203164,0.05293238],"study_design_scores_gemma":[0.00003141209,0.0002928886,0.0007207356,0.000007197761,0.0000145078,0.00005533109,0.00003066992,0.9737592,0.01941952,0.001580077,0.00407575,0.00001270348],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2519854,0.00008523068,0.723514,0.0001684786,0.00007159874,0.0002469861,0.0002591514,0.004086199,0.019583],"genre_scores_gemma":[0.9519139,0.00004199982,0.04525059,0.00002168174,0.000004254683,0.0001013607,0.0001625932,0.00009220494,0.002411531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003798629,"threshold_uncertainty_score":0.01270771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02782652135873574,"score_gpt":0.2756102216169243,"score_spread":0.2477837002581886,"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."}}