{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001539891,0.0003776739,0.0002823851,0.0000801876,0.0003304105,0.00004698155,0.0004285506,0.0002344908,0.00460769],"category_scores_gemma":[0.00001572322,0.0003198353,0.000135486,0.0001405472,0.0006370957,0.0004277992,0.0004056641,0.00009446222,0.0001854679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008898648,"about_ca_system_score_gemma":0.00001679878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004039473,"about_ca_topic_score_gemma":0.00001324344,"domain_scores_codex":[0.9977695,0.00005751267,0.0003618945,0.000637681,0.0003098888,0.0008635187],"domain_scores_gemma":[0.9990904,0.00003159506,0.0001551382,0.0005618889,0.000003074476,0.0001579496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007215279,0.01507706,0.3725268,0.0001739748,0.001059602,0.001234214,0.008224579,0.003291431,0.03603679,0.05867498,0.04255676,0.4539286],"study_design_scores_gemma":[0.005165116,0.003749887,0.005066817,0.000009577286,0.00009649914,0.0000156726,0.004268217,0.0007581119,0.5769833,0.007447507,0.3954326,0.001006698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882201,0.0001805109,0.0001924772,0.0002366826,0.000214492,0.001278403,0.0000214325,0.0003239695,0.00933195],"genre_scores_gemma":[0.9730291,0.00005241819,0.00121978,0.00006710598,0.00007068881,0.001132171,0.00009404777,0.00005577302,0.02427889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5409465,"threshold_uncertainty_score":0.9999254,"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."}}