{"id":"W2045816714","doi":"10.2166/wst.2008.485","title":"Modelling nitrite in wastewater treatment systems: a discussion of different modelling concepts","year":2008,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; EnviroSim (Canada)","funders":"","keywords":"Nitrite; Wastewater; Nitrification; Denitrification; Biochemical engineering; Sewage treatment; Simultaneous nitrification-denitrification; Environmental science; Environmental engineering; Computer science; Engineering; Nitrate; Chemistry; Nitrogen","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":[0.0001840918,0.0002800715,0.000398255,0.0005817478,0.0002621699,0.00002195059,0.0005500461,0.0001399552,0.00005543789],"category_scores_gemma":[0.00000205817,0.0001333264,0.00007402627,0.0007454723,0.001462579,0.0003567713,0.0003588501,0.0001173887,0.0001102029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004754842,"about_ca_system_score_gemma":0.00001986157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006289346,"about_ca_topic_score_gemma":0.00001873601,"domain_scores_codex":[0.9976488,0.00003854672,0.0004421901,0.0006563717,0.000441887,0.0007721971],"domain_scores_gemma":[0.9992997,0.000008567302,0.00007915637,0.0004951033,0.00001560531,0.0001018352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000485286,0.0004060272,0.03868905,0.000009608458,0.0000102536,0.0001065596,0.004008132,0.5376887,0.4184857,0.0001179339,0.000003174897,0.0004263099],"study_design_scores_gemma":[0.0007294953,0.0002927379,0.00005458138,0.00003802173,0.00001218427,0.00008589888,0.0003889722,0.304564,0.6927264,0.0007306261,0.0001684049,0.0002086451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996476,0.00007210019,0.002372485,0.0002379299,0.0001214567,0.0004050999,0.000002984343,0.00008651109,0.0002254354],"genre_scores_gemma":[0.996196,0.0000664302,0.002922545,0.000004458981,0.0000113598,0.00006053743,0.000004631663,0.00001534278,0.0007187495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2742407,"threshold_uncertainty_score":0.5436894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02318859880272841,"score_gpt":0.2312687174481457,"score_spread":0.2080801186454173,"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."}}