{"id":"W2022862294","doi":"10.1002/ep.10547","title":"Combined MBBR‐MF for industrial wastewater treatment","year":2011,"lang":"en","type":"article","venue":"Environmental Progress & Sustainable Energy","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Alum; Chemistry; Ferric; Chloride; Effluent; Moving bed biofilm reactor; Coagulation; Fouling; Membrane fouling; Wastewater; Pulp and paper industry; Filtration (mathematics); Chromatography; Ultrafiltration (renal); Membrane; Nuclear chemistry; Environmental engineering; Inorganic chemistry; Biofilm; Environmental science; Organic chemistry; Biochemistry","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":[0.00041685,0.0004924106,0.0005881722,0.000412769,0.0003310276,0.0005353178,0.0004148672,0.0004918395,0.001457836],"category_scores_gemma":[0.0002500744,0.0002125446,0.0004298894,0.0002086952,0.0001012841,0.0003707512,0.000367837,0.000460148,0.0009013698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00045021,"about_ca_system_score_gemma":0.0003588994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002118766,"about_ca_topic_score_gemma":0.003446567,"domain_scores_codex":[0.9997092,0.00004306696,0.00001593513,0.00006007159,0.0001317479,0.00003987482],"domain_scores_gemma":[0.9999368,0.000009591095,0.000008017703,0.000004710148,0.00002420996,0.00001671855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001149984,0.0000978594,0.0003497101,0.0001236364,0.00001276728,0.00002826668,0.000008880352,0.0002894066,0.9882296,0.00002950847,0.0001217107,0.01059373],"study_design_scores_gemma":[0.00005957884,0.001247355,0.002929413,0.00001890924,0.00007259514,0.0001669127,0.00001788168,0.0029652,0.9883397,0.000046178,0.004126323,0.00000992385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740292,0.006235531,0.01622448,0.000231426,0.000113965,0.0001153865,0.0002162421,0.0004418281,0.002392029],"genre_scores_gemma":[0.9751637,0.001340121,0.01935903,0.00007216854,0.00003471178,0.00005810109,0.0002717829,0.00002861228,0.003671661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002118766,"threshold_uncertainty_score":0.004876971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02321211315156059,"score_gpt":0.2175968968797541,"score_spread":0.1943847837281935,"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."}}