{"id":"W2081812798","doi":"10.1115/1.1589029","title":"Nonlinear Predictive Control of Transients in Automotive Variable Cam Timing Engine Using Nonlinear Parametric Approximation","year":2003,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Automotive industry; Control theory (sociology); Nonlinear system; Feed forward; Control engineering; Controller (irrigation); Parametric statistics; Setpoint; Model predictive control; Computer science; Nonlinear control; Automotive engine; Engineering; Artificial neural network; Control (management); Automotive engineering; Mathematics; 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":[0.001994946,0.0002553663,0.000859083,0.0005755158,0.00004179064,0.00003453505,0.0001059543,0.0001408654,0.000002024179],"category_scores_gemma":[0.0004345495,0.0002377656,0.00009651067,0.0004852157,0.0000243508,0.0003920576,0.000002955568,0.0002529096,3.29605e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005852344,"about_ca_system_score_gemma":0.0001115235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002064242,"about_ca_topic_score_gemma":0.000004892447,"domain_scores_codex":[0.9972502,0.000269022,0.001320539,0.0001668506,0.0006960541,0.0002973139],"domain_scores_gemma":[0.9980734,0.0001342704,0.0006462571,0.0001402107,0.0009043479,0.0001015103],"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":[0.000130516,0.00009273915,0.001494943,0.0002571774,0.0003666989,0.000004555839,0.0002036377,0.9778445,0.0191356,0.00002763825,8.291331e-7,0.0004412108],"study_design_scores_gemma":[0.00848519,0.0002037399,0.0003711161,0.0007006871,0.0002444435,0.0000602172,0.0003116711,0.9891787,0.0002212897,0.00002011288,0.00002458162,0.0001782815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07665199,0.004482534,0.9171418,0.000007109255,0.0006459338,0.000921475,0.00002889963,0.00002856479,0.00009168747],"genre_scores_gemma":[0.9916391,0.00004681208,0.00816602,0.000005527957,0.00007826717,0.00001912388,0.000001983533,0.00003888524,0.000004266043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9149871,"threshold_uncertainty_score":0.9695801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188206533389316,"score_gpt":0.2085163733371209,"score_spread":0.1966343080032278,"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."}}