{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00128032,0.0001442032,0.000456995,0.0002366172,0.00004335188,0.00006919479,0.000166737,0.000102363,0.000003577141],"category_scores_gemma":[0.000154958,0.0001271739,0.00007855734,0.0002591565,0.00004829652,0.0003716314,0.000005977943,0.0002015636,0.00000147785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008784368,"about_ca_system_score_gemma":0.0001228766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000059041,"about_ca_topic_score_gemma":0.000003744194,"domain_scores_codex":[0.9980429,0.00003034607,0.001116123,0.0001266449,0.0004616992,0.0002223221],"domain_scores_gemma":[0.9978513,0.0001352008,0.0004876751,0.0001124996,0.001290939,0.0001224094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003263853,0.00007371906,0.004770548,0.002892327,0.0002277664,0.00001326768,0.0004337548,0.005136588,0.9809511,0.0000939346,0.0000086544,0.005071986],"study_design_scores_gemma":[0.01047557,0.0001863769,0.007189667,0.00135611,0.0006119331,0.0008909417,0.002517098,0.6310111,0.3450057,0.00006251528,0.0001726231,0.0005204027],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7410758,0.001882634,0.2565075,0.0000261915,0.0001946619,0.0002318021,0.00001907436,0.00002599058,0.00003638146],"genre_scores_gemma":[0.9993,0.00001463119,0.0001949333,0.000005503475,0.0004478308,0.000004012491,0.000001774974,0.00002443705,0.000006901273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6359453,"threshold_uncertainty_score":0.5185999,"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."}}