{"id":"W2795437321","doi":"10.4271/2018-01-1458","title":"Automatic Calibrations Generation for Powertrain Controllers Using MapleSim","year":2018,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Powertrain; Automotive engineering; Computer science; Control engineering; Control theory (sociology); Engineering; Torque; Control (management); Artificial intelligence; Physics","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":[0.0004190064,0.0008860417,0.0004640022,0.0005603448,0.0004633571,0.0006296602,0.000934894,0.0004449346,0.01372301],"category_scores_gemma":[0.001476704,0.0003829892,0.0002995361,0.0002857772,0.0001811213,0.0008169642,0.0008959724,0.0006667014,0.00241165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003515505,"about_ca_system_score_gemma":0.0005631722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002188065,"about_ca_topic_score_gemma":0.002969787,"domain_scores_codex":[0.9997845,0.00003961038,0.00001018504,0.0000510103,0.00008615368,0.00002854123],"domain_scores_gemma":[0.9996352,0.0001002451,0.000035851,0.00007919106,0.0001302071,0.0000192184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009788027,0.0002498737,0.002517469,0.000369817,0.000120643,0.0002043025,0.0003559199,0.3309734,0.05018607,0.007766192,0.02054465,0.5857329],"study_design_scores_gemma":[0.00006302426,0.00008061257,0.0006234799,0.00001754551,0.00001471758,0.00004365135,0.0000273615,0.949737,0.03772804,0.001929893,0.009715381,0.00001938264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03758885,0.0001157074,0.9093611,0.0001209712,0.0001382469,0.00008323472,0.0003131981,0.04400754,0.008271106],"genre_scores_gemma":[0.8009222,0.00005413699,0.1908953,0.00007902227,0.00002476602,0.000176899,0.000572845,0.002353335,0.004921564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01372301,"threshold_uncertainty_score":0.04590803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104283167809244,"score_gpt":0.2592115262610409,"score_spread":0.2381686945829485,"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."}}