{"id":"W3041036705","doi":"10.1177/1468087420936949","title":"A correlation-based model order reduction approach for a diesel engine NO <sub>x</sub> and brake mean effective pressure dynamic model using machine learning","year":2020,"lang":"en","type":"article","venue":"International Journal of Engine Research","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Support vector machine; Reduction (mathematics); Model order reduction; Mean effective pressure; Diesel engine; Artificial neural network; Algorithm; Relevance vector machine; Nonlinear system; Artificial intelligence; Computer science; Machine learning; Engineering; Mathematics; Automotive 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005927761,0.00118139,0.001139291,0.0005720977,0.0007077806,0.00079817,0.00103337,0.0006614247,0.002393183],"category_scores_gemma":[0.001108686,0.0006720607,0.001718642,0.0005569718,0.0003787283,0.000672473,0.0005334459,0.001904195,0.0009041723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091424,"about_ca_system_score_gemma":0.002465939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0247256,"about_ca_topic_score_gemma":0.02008007,"domain_scores_codex":[0.9995909,0.0000684019,0.00003125622,0.00009095962,0.0001754504,0.00004307878],"domain_scores_gemma":[0.9994536,0.0001980627,0.00007977499,0.00004997112,0.0001987705,0.00001982965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004728142,0.000078611,0.0007266783,0.0001473932,0.00006565652,0.00009946786,0.00006963778,0.9166277,0.005767185,0.0041218,0.001010402,0.07123815],"study_design_scores_gemma":[0.000002296424,0.00001569338,0.0001172547,0.000002375189,0.000006883766,0.000008673609,0.000003959594,0.9983442,0.0006408478,0.000418417,0.0004343523,0.000004947658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00894451,0.0002095947,0.9878749,0.0001038926,0.00003612288,0.00005987365,0.00006161033,0.0007920272,0.001917499],"genre_scores_gemma":[0.6250938,0.0006340112,0.3636934,0.000183328,0.00008808317,0.0005328331,0.0008242134,0.0003285668,0.008621787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0247256,"threshold_uncertainty_score":0.04916334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03523508203400916,"score_gpt":0.3259594662530511,"score_spread":0.290724384219042,"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."}}