{"id":"W2040542910","doi":"10.1016/j.memsci.2011.06.017","title":"Performance of the submerged membrane electro-bioreactor (SMEBR) with iron electrodes for wastewater treatment and fouling reduction","year":2011,"lang":"en","type":"article","venue":"Journal of Membrane Science","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":140,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Membrane fouling; Membrane bioreactor; Filtration (mathematics); Fouling; Membrane; Bioreactor; Electrokinetic phenomena; Electrode; Wastewater; Effluent; Materials science; Chemistry; Chromatography; Chemical engineering; Environmental engineering; Environmental science; Engineering; Nanotechnology","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":[0.0008182604,0.0001631847,0.0002428238,0.0001282074,0.0002625539,0.00002508112,0.0005220713,0.0000507204,0.00007500745],"category_scores_gemma":[0.00007709512,0.00008676275,0.00006769958,0.0005482535,0.001005186,0.0007805987,0.00006178111,0.0001324171,0.000002119531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782284,"about_ca_system_score_gemma":0.00009530273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004148591,"about_ca_topic_score_gemma":0.00001493528,"domain_scores_codex":[0.9984854,0.00003313921,0.0003939911,0.0002548884,0.0004864801,0.0003460606],"domain_scores_gemma":[0.9990152,0.00002603318,0.0005213431,0.0002902984,0.00006715624,0.00007993059],"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":[0.0003240271,0.00006788116,0.003795204,0.00002109149,0.00001014324,5.160284e-7,0.0007076496,0.0003595819,0.9928284,0.00004143116,0.000009544258,0.001834475],"study_design_scores_gemma":[0.000589597,0.00168993,0.009177797,0.00003241951,0.00004721195,0.0002184293,0.000209583,0.0007022201,0.9870552,0.0000514578,0.0001152929,0.0001108649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986491,0.00004074688,0.0000321514,0.0002559984,0.0001354192,0.0003191881,0.000001127156,0.00001484405,0.0005513798],"genre_scores_gemma":[0.9954674,0.000221839,0.00407126,0.00001168432,0.00002778082,0.000009980188,2.060644e-7,0.00001025564,0.0001796234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00577326,"threshold_uncertainty_score":0.3703648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731409238126886,"score_gpt":0.2212488608086801,"score_spread":0.2039347684274112,"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."}}