{"id":"W2509963146","doi":"10.1016/j.jenvman.2016.07.098","title":"Energy-positive food wastewater treatment using an anaerobic membrane bioreactor (AnMBR)","year":2016,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Ministero dello Sviluppo Economico; Ontario Ministry of Economic Development and Innovation","keywords":"Chemistry; Wastewater; Biogas; Membrane fouling; Methane; Anaerobic digestion; Bioreactor; Sewage treatment; Fouling; Pulp and paper industry; Environmental engineering; Membrane; Waste management; Environmental science; Biochemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001789254,0.0002949326,0.0002888448,0.0001648961,0.0001109116,0.00003539293,0.0004002475,0.00008193882,0.002727111],"category_scores_gemma":[0.000002925136,0.0001823621,0.0001563872,0.000109378,0.00025844,0.0008063244,0.0002574176,0.0000628156,0.0001462469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125841,"about_ca_system_score_gemma":0.000004557974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002746357,"about_ca_topic_score_gemma":0.00002188518,"domain_scores_codex":[0.9981385,0.00009111378,0.0005331943,0.0003472781,0.0005353853,0.0003545494],"domain_scores_gemma":[0.9989708,0.00002689731,0.0004089381,0.0004182136,0.000002699138,0.0001724489],"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":[0.0001405647,0.001041902,0.002427633,0.000005463425,0.0002775997,0.0001458155,0.0001585526,0.0005122032,0.9396126,0.0002831375,0.0001645815,0.05522997],"study_design_scores_gemma":[0.002970524,0.003893573,0.02874663,0.00007808192,0.0002564167,0.0001657812,0.0006132655,0.0001815359,0.9495412,0.0007303975,0.01225697,0.0005656718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967463,0.00005959954,0.00135567,0.0002653132,0.0001768124,0.0002100716,0.00002678306,0.00003008305,0.00112934],"genre_scores_gemma":[0.9939725,0.0005202831,0.004114668,0.00009931058,0.00005923862,0.000008765309,0.000003972152,0.0000310047,0.001190281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0546643,"threshold_uncertainty_score":0.9981845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616380750838111,"score_gpt":0.2185394093563092,"score_spread":0.2023756018479281,"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."}}