{"id":"W2171707572","doi":"10.2166/hydro.2011.041","title":"A practical protocol for calibration of nutrient removal wastewater treatment models","year":2011,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Calibration; Bottleneck; Identifiability; Activated sludge model; Protocol (science); Computer science; Sensitivity (control systems); Mathematical optimization; Function (biology); Estimation theory; Set (abstract data type); Monte Carlo method; Data mining; Sewage treatment; Activated sludge; Machine learning; Engineering; Algorithm; Mathematics; Statistics; Environmental engineering","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":[0.0002494046,0.0001450514,0.0002586733,0.00006220461,0.0000473291,0.00001686465,0.0001180842,0.0000569212,0.0002032285],"category_scores_gemma":[0.00001402791,0.00008922076,0.0001910706,0.00007207594,0.0000647937,0.001061535,0.00004116772,0.00005826362,0.00001147836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157891,"about_ca_system_score_gemma":0.00004782648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001486979,"about_ca_topic_score_gemma":0.0000031474,"domain_scores_codex":[0.9985741,0.00002657742,0.0008018646,0.00006394965,0.000338387,0.0001951351],"domain_scores_gemma":[0.9990062,0.00003395358,0.0006700358,0.0001532296,0.00002327336,0.0001133558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.07461596,0.06128788,0.01839218,0.004185554,0.007026711,0.00265089,0.3147278,0.1221074,0.2294078,0.03708641,0.1009467,0.02756465],"study_design_scores_gemma":[0.007015855,0.006202935,0.0000556181,0.00009232892,0.0002295402,0.001598234,0.0006737526,0.4358265,0.5306871,0.01070097,0.006648177,0.0002690221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9477278,0.000002476702,0.009911622,0.0001502646,0.0001000948,0.03730058,0.00001213604,0.00001464436,0.004780378],"genre_scores_gemma":[0.2523556,0.00000578522,0.7401346,0.00005340923,0.0001057519,0.00676908,0.000006969331,0.00003347579,0.0005352976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.730223,"threshold_uncertainty_score":0.3638317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07273880827507558,"score_gpt":0.2944959920529437,"score_spread":0.2217571837778682,"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."}}