{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006726075,0.001260034,0.001042142,0.001285315,0.001018733,0.001079512,0.001806561,0.00178243,0.01063306],"category_scores_gemma":[0.01571079,0.001115432,0.001571537,0.001229073,0.000711426,0.001087664,0.003068556,0.003229541,0.003773484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000816169,"about_ca_system_score_gemma":0.002077017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001102411,"about_ca_topic_score_gemma":0.001614339,"domain_scores_codex":[0.9956596,0.00164391,0.0005327179,0.0005660211,0.001417105,0.0001806355],"domain_scores_gemma":[0.9942147,0.001726881,0.0003441731,0.001977187,0.001648447,0.00008850252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004139912,0.0005551503,0.001859046,0.001002957,0.0001765883,0.0009352687,0.0007757482,0.4310348,0.1764987,0.09212043,0.0108562,0.2837712],"study_design_scores_gemma":[0.0002173409,0.000681914,0.001874075,0.0002547913,0.0001135134,0.0005878072,0.0001868059,0.5871447,0.244081,0.06405865,0.1005242,0.0002751762],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.003294944,0.00004214965,0.9929984,0.00007114615,0.00003787354,0.0006403243,0.000296726,0.0009838496,0.001634534],"genre_scores_gemma":[0.04767554,0.0001878893,0.9443111,0.00008005096,0.00002150363,0.00412107,0.001054328,0.0003042377,0.002244298],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.01063306,"threshold_uncertainty_score":0.03557128,"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."}}