{"id":"W2319246501","doi":"10.2166/wst.2016.050","title":"Design of a generalized predictive controller for a biological wastewater treatment plant","year":2016,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Setpoint; Activated sludge; Model predictive control; Sewage treatment; Benchmark (surveying); Wastewater; Bioreactor; Control theory (sociology); Controller (irrigation); Process control; Filter (signal processing); Process (computing); Process engineering; Engineering; Environmental engineering; Computer science; Control (management); Chemistry; 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":[0.0001704603,0.0001299353,0.0002648258,0.0003389541,0.00005589299,0.000007229782,0.0002413421,0.0001194217,0.000005573789],"category_scores_gemma":[0.00003377425,0.00005781124,0.00003204915,0.0001881259,0.0003685126,0.0001247305,0.00003277335,0.0000216388,0.000007412293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592031,"about_ca_system_score_gemma":0.00001588097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000152128,"about_ca_topic_score_gemma":5.242632e-7,"domain_scores_codex":[0.9990409,0.0000146936,0.0002257168,0.0002511891,0.00007943533,0.0003880638],"domain_scores_gemma":[0.9996277,0.00003769301,0.00003333529,0.0001986745,0.00006880713,0.00003376552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009360992,0.0000129817,0.00006732358,0.000002364473,0.00001872999,0.000001012414,0.000106168,0.04781335,0.9492767,0.0006010169,0.00000572514,0.002001056],"study_design_scores_gemma":[0.002069409,0.0006147986,0.000005403469,0.00001395769,0.000008851718,0.00001058039,0.00001906713,0.1745175,0.8196345,0.00252234,0.0004848428,0.00009870754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.106803,0.0000684715,0.8915673,0.0002453784,0.000128968,0.0008081628,0.00002415637,0.0003332627,0.00002130322],"genre_scores_gemma":[0.9853051,0.00002895044,0.01398863,0.000005314666,0.00001763522,0.0005824252,0.000001719216,0.00001098097,0.0000592793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8785021,"threshold_uncertainty_score":0.2357474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778051911375424,"score_gpt":0.2189935790299554,"score_spread":0.2012130599162011,"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."}}