{"id":"W4393234552","doi":"10.5206/mase/17134","title":"Unraveling the role of inert biomass in membrane aerated biofilm reactors for simultaneous nitrification and denitrification","year":2024,"lang":"en","type":"article","venue":"Mathematics in Applied Sciences and Engineering","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aeration; Nitrification; Biofilm; Denitrification; Biomass (ecology); Simultaneous nitrification-denitrification; Inert; Chemistry; Membrane reactor; Pulp and paper industry; Environmental science; Environmental chemistry; Environmental engineering; Membrane; Ecology; Nitrogen; Biology; Engineering; Bacteria; Biochemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002893189,0.0005763296,0.0006044878,0.0001735817,0.0003769851,0.0009100504,0.0006373823,0.001192026,0.0004711708],"category_scores_gemma":[0.0005508295,0.0003078229,0.0007399638,0.0001787044,0.0004428405,0.0008320188,0.0009056726,0.0006401068,0.00008805201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006653389,"about_ca_system_score_gemma":0.0009093388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009231194,"about_ca_topic_score_gemma":0.005122047,"domain_scores_codex":[0.9998727,0.00002974711,0.000007131888,0.00003161696,0.00003400377,0.00002480226],"domain_scores_gemma":[0.9998772,0.00005936953,0.00001968129,0.00001232824,0.00001480407,0.00001664314],"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":[0.00005194647,0.00004719444,0.001425185,0.0000916369,0.0000282168,0.0001795367,0.00006465613,0.9418069,0.0490172,0.004058138,0.00007316242,0.003156335],"study_design_scores_gemma":[0.000009170706,0.00004371125,0.0004233574,0.000006126201,0.00001026355,0.00001708911,0.00002773265,0.9945985,0.003396476,0.001027133,0.0004309922,0.000009401947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8692622,0.001343915,0.1213223,0.0006111921,0.0001101345,0.00004718044,0.000273607,0.0001192299,0.006910137],"genre_scores_gemma":[0.986378,0.0006324216,0.01141867,0.00004886921,0.00001255834,0.0000511286,0.00007790807,0.00001613562,0.001364299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009231194,"threshold_uncertainty_score":0.01835489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01046006654092206,"score_gpt":0.2124028441513244,"score_spread":0.2019427776104024,"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."}}