{"id":"W1969419131","doi":"10.1016/s0959-1524(01)00002-6","title":"Model predictive control using an extended ARMarkov model","year":2002,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Model predictive control; Control theory (sociology); Controller (irrigation); Markov chain; Consistency (knowledge bases); Markov process; Computer science; Identification (biology); Markov model; Process (computing); Mathematics; Control (management); Artificial intelligence; Machine learning; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002888782,0.0002844979,0.0006280502,0.0002315009,0.00008930964,0.00006448434,0.0003006939,0.0001433584,0.00001724122],"category_scores_gemma":[0.0001058527,0.0002621929,0.0001451003,0.0001599342,0.00003352113,0.001668346,0.000005657505,0.0003920228,0.000002294024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002115057,"about_ca_system_score_gemma":0.00006077977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.115478e-7,"about_ca_topic_score_gemma":0.000001224329,"domain_scores_codex":[0.9980537,0.00005898986,0.0008514549,0.0001794784,0.0004698605,0.0003865215],"domain_scores_gemma":[0.9983489,0.00005565472,0.0004650532,0.0002062936,0.0006796808,0.0002444385],"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":[0.0001950492,0.00009181739,0.00004926079,0.00004999402,0.0001803043,0.00001391417,0.0003996539,0.9891071,0.008623708,0.0000497997,0.00004702735,0.001192391],"study_design_scores_gemma":[0.00654688,0.0001348612,0.00001203806,0.00007687572,0.0002101759,0.00009641609,0.00008635536,0.9910544,0.0001473884,0.001382797,0.000005800048,0.0002460395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01829032,0.001409864,0.9789593,0.00005701532,0.0002001369,0.0003790639,0.00003321147,0.0001312146,0.000539855],"genre_scores_gemma":[0.9911581,0.00004952457,0.008172248,0.0001258408,0.000353768,0.00002236517,7.975001e-7,0.0000798059,0.00003754609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9728678,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076285776290253,"score_gpt":0.2476948779197096,"score_spread":0.2269320201568071,"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."}}