{"id":"W2933523471","doi":"10.1016/j.biteb.2019.03.015","title":"Submerged aerobic granular sludge membrane bioreactor (AGMBR): Organics and nutrients (nitrogen and phosphorus) removal","year":2019,"lang":"en","type":"article","venue":"Bioresource Technology Reports","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Cumming School of Medicine, University of Calgary; Chinese Academy of Sciences; U.S. Environmental Protection Agency","keywords":"Hydraulic retention time; Chemistry; Chemical oxygen demand; Membrane bioreactor; Nitrosomonas; Nitrification; Nutrient; Anoxic waters; Nitrospira; Environmental chemistry; Bioreactor; Denitrification; Phosphorus; Nitrogen; Pulp and paper industry; Wastewater; Denitrifying bacteria; Environmental engineering; Environmental science; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001634872,0.0005583288,0.0006338868,0.0002521822,0.0002571435,0.000390537,0.0003073666,0.000530371,0.0004141815],"category_scores_gemma":[0.00009807537,0.0001216758,0.0004326981,0.0002674622,0.0001001383,0.0003322778,0.000298588,0.0003844037,0.0002691284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158257,"about_ca_system_score_gemma":0.0005491307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004158368,"about_ca_topic_score_gemma":0.006183644,"domain_scores_codex":[0.9997984,0.00002191447,0.00002002031,0.00004977619,0.00006357893,0.00004646745],"domain_scores_gemma":[0.9999424,0.000007290927,0.00001162148,0.000004761272,0.00001206109,0.00002193042],"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":[0.0001318872,0.00004664213,0.0001572895,0.00003421907,0.00000464662,0.00003240081,0.000005627271,0.00006574973,0.9977489,0.00001374024,0.00002255388,0.001736332],"study_design_scores_gemma":[0.00002905387,0.0006678866,0.004694549,0.000004973157,0.00003642225,0.0001055859,0.00003478619,0.001486854,0.9920067,0.00003212295,0.0008904539,0.00001054406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954759,0.0009307791,0.002391435,0.0001009992,0.00004789052,0.00001997671,0.0002669239,0.00009595937,0.0006700779],"genre_scores_gemma":[0.9932799,0.0005697086,0.003940046,0.00003753512,0.0000136607,0.00001179613,0.0002522216,0.000008821039,0.001886404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004158368,"threshold_uncertainty_score":0.008268356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004304845168936922,"score_gpt":0.1823130175177046,"score_spread":0.1780081723487677,"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."}}