{"id":"W1971360386","doi":"10.1016/j.jwpe.2014.03.007","title":"Optimization of coagulant dose for biopolymer removal: Impact on ultrafiltration fouling and retention of organic micropollutants","year":2014,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; University of Toronto; Canadian Water Network","keywords":"Chemistry; Ultrafiltration (renal); Fouling; Membrane fouling; Biopolymer; Coagulation; Alum; Chromatography; Organic matter; Water treatment; Dissolved organic carbon; Membrane; Natural organic matter; Permeation; Environmental chemistry; Polymer; Environmental engineering; Organic chemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002965381,0.00008167113,0.0001647424,0.0001027983,0.00002052787,0.00001052268,0.00008483725,0.0000517616,0.00003040115],"category_scores_gemma":[0.00009460428,0.00005426018,0.00004048986,0.00008921707,0.00002927817,0.0002602481,0.00001132082,0.0000565367,4.667223e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003330799,"about_ca_system_score_gemma":0.000004934101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002568065,"about_ca_topic_score_gemma":4.281376e-7,"domain_scores_codex":[0.9993231,0.000008849729,0.0003425813,0.00007804163,0.0001503122,0.00009715268],"domain_scores_gemma":[0.9996169,0.00002367941,0.0002306427,0.00006733703,0.0000356344,0.00002585904],"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":[0.000039082,0.00001062753,0.0001818135,0.00005731581,0.000008526,2.325343e-7,0.0001393643,0.3687876,0.6302652,0.00000388862,0.000001734286,0.0005047227],"study_design_scores_gemma":[0.0003248263,0.0002673991,0.0005274143,0.00006927233,0.00001718209,0.00003849556,0.00003382228,0.1266834,0.8719167,0.00005950485,0.000005360302,0.00005657071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8596435,0.00002376377,0.140139,0.00003676131,0.00004413801,0.00008595149,0.000001742354,0.00001107458,0.0000139767],"genre_scores_gemma":[0.9917312,0.00002819607,0.008196505,0.000003182424,0.00001826684,0.000001066902,0.000001956932,0.00001104885,0.000008592866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2421041,"threshold_uncertainty_score":0.2212666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009167985556210652,"score_gpt":0.2347076782778431,"score_spread":0.2255396927216325,"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."}}