{"id":"W1201483785","doi":"10.2166/wrd.2015.058","title":"Anaerobic membrane bioreactor for high-strength wastewater treatment: batch and continuous operation comparison","year":2015,"lang":"en","type":"article","venue":"Journal of Water Reuse and Desalination","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"","keywords":"Bioreactor; Wastewater; Effluent; Filtration (mathematics); Chemical oxygen demand; Membrane fouling; Chemistry; Pulp and paper industry; Membrane; Anaerobic exercise; Membrane bioreactor; Chromatography; Sewage treatment; Fouling; Hydraulic retention time; Membrane reactor; Environmental engineering; Environmental science; Biology; Biochemistry; Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003154206,0.0001083538,0.0002092217,0.00008095706,0.0000638412,0.00008386448,0.00009246267,0.00007717834,0.00002837939],"category_scores_gemma":[0.00006358763,0.00006444297,0.00002434726,0.00004085153,0.0000626874,0.0005223313,0.00004741324,0.00004990239,0.00001025219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009154443,"about_ca_system_score_gemma":0.000007491174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006267053,"about_ca_topic_score_gemma":0.00003507311,"domain_scores_codex":[0.9992152,0.00004056083,0.0003343933,0.0001283384,0.0001603785,0.0001211027],"domain_scores_gemma":[0.9995724,0.00002878682,0.0001536757,0.0001245606,0.00004425791,0.00007630208],"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":[0.0005768078,0.00039476,0.01847897,0.00003297883,0.00009034725,0.00001354086,0.008385996,0.0009690403,0.9466124,0.0004188633,0.003460242,0.02056611],"study_design_scores_gemma":[0.002308999,0.00136685,0.001539103,0.00001213677,0.00005722432,0.00004027687,0.0004779754,0.001676212,0.9857405,0.001129013,0.005529024,0.0001227058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997769,0.00007495411,0.0003749223,0.001399983,0.0001062717,0.0001988365,0.000003988711,0.0000189715,0.00005309014],"genre_scores_gemma":[0.9934036,0.0000992626,0.006156079,0.00002576571,0.00004745058,0.000007184432,0.00002325313,0.000008498719,0.0002289028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03912814,"threshold_uncertainty_score":0.2627908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837240634283291,"score_gpt":0.267630912512137,"score_spread":0.2392585061693041,"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."}}