{"id":"W2017090746","doi":"10.1007/s00449-012-0806-1","title":"Biological nitrogen and phosphorus removal in membrane bioreactors: model development and parameter estimation","year":2012,"lang":"en","type":"article","venue":"Bioprocess and Biosystems Engineering","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Canada Research Chairs","keywords":"Activated sludge model; Membrane bioreactor; Biochemical engineering; Membrane fouling; Bottleneck; Enhanced biological phosphorus removal; Phosphorus; Fouling; Bioreactor; Sewage treatment; Activated sludge; Mathematical model; Wastewater; Process (computing); Environmental science; Process engineering; Computer science; Environmental engineering; Engineering; Membrane; Chemistry; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001640846,0.0001652687,0.0001602699,0.0000464318,0.00004543408,0.00003499931,0.00003886482,0.00008736427,0.000003698857],"category_scores_gemma":[0.000009110565,0.0001189111,0.00001169863,0.0000915461,0.00003431979,0.0001969369,0.000076428,0.00004795853,0.000004347408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000437529,"about_ca_system_score_gemma":0.000002964005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007270903,"about_ca_topic_score_gemma":0.000004202737,"domain_scores_codex":[0.9992552,0.000006851184,0.0001727592,0.0002092077,0.00009329853,0.0002626955],"domain_scores_gemma":[0.9997736,0.00001666054,0.00003089345,0.00005479078,0.000002459716,0.0001215578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006418418,0.00016173,0.6199756,0.0003106286,0.0000569063,0.00002801989,0.002819771,0.0008610312,0.3428817,0.000159535,0.000005649779,0.03267523],"study_design_scores_gemma":[0.001653254,0.0001207407,0.1006075,0.0002552079,0.00004849579,0.0005959106,0.0003451281,0.3270763,0.5664317,0.0001075105,0.001606875,0.001151318],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978119,0.001672718,0.0002614233,0.00001058959,0.0000330857,0.0001486288,0.000002769766,0.00003429061,0.00002457071],"genre_scores_gemma":[0.9746429,0.00007342929,0.02521196,0.000006558939,0.00001772033,0.00001778904,0.000006676671,0.000009499053,0.0000134874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5193681,"threshold_uncertainty_score":0.4849053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658897801257159,"score_gpt":0.2013045210502341,"score_spread":0.1847155430376625,"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."}}