{"id":"W1980944302","doi":"10.1016/j.jprocont.2007.02.001","title":"Nonlinear system identification and control of chemical processes using fast orthogonal search","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Ontario Institute of Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Control theory (sociology); Linearization; Controller (irrigation); System identification; Chemical process; Identification (biology); Process (computing); Inverse; Nonlinear control; Control system; Process control; Nonlinear system identification; Computer science; Inverse system; Mathematics; Control (management); Engineering; Artificial intelligence; Data modeling; 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":[0.001270362,0.0004865174,0.000836521,0.0004227702,0.0005349143,0.0006073421,0.000430778,0.0006217625,0.0008842978],"category_scores_gemma":[0.002507117,0.0004028522,0.000481076,0.0004632916,0.0005986503,0.0008740812,0.0007672438,0.0007096197,0.0002389767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003133788,"about_ca_system_score_gemma":0.001179988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003370586,"about_ca_topic_score_gemma":0.003014977,"domain_scores_codex":[0.9996361,0.0001468958,0.00002659214,0.00004321723,0.0001118311,0.00003541393],"domain_scores_gemma":[0.9992697,0.0004093564,0.00009544858,0.00006243591,0.0001476312,0.00001550236],"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":[0.0003759977,0.000129358,0.0005301664,0.0002260969,0.0001031908,0.00006208416,0.0001004494,0.807799,0.01841686,0.01911536,0.0006040607,0.1525373],"study_design_scores_gemma":[0.00001097638,0.00002193914,0.00007236076,0.000002165933,0.000004209428,0.000007577071,0.000003046069,0.997136,0.001460409,0.001129095,0.0001486899,0.000003480784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03027174,0.0003670437,0.9677728,0.000101385,0.00005275536,0.00004191632,0.00002050779,0.0001626952,0.001209174],"genre_scores_gemma":[0.6938874,0.0005126584,0.3023756,0.00005000628,0.00004263669,0.0002308431,0.00007778494,0.00004808094,0.002775085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003370586,"threshold_uncertainty_score":0.006718457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009960057358899285,"score_gpt":0.2501679591636727,"score_spread":0.2402079018047734,"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."}}