{"id":"W4386024461","doi":"10.1016/j.seppur.2023.124877","title":"Leveraging coagulation mechanisms to reduce fouling and increase natural organic matter removal during coagulation/flocculation-ultrafiltration treatment","year":2023,"lang":"en","type":"article","venue":"Separation and Purification Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Flocculation; Coagulation; Alum; Fouling; Chemistry; Membrane fouling; Ultrafiltration (renal); Adsorption; Natural organic matter; Humic acid; Filtration (mathematics); Water treatment; Chemical engineering; Organic matter; Membrane; Chromatography; Pulp and paper industry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002465089,0.0002417636,0.000208974,0.0005387019,0.0005368117,0.0001086194,0.0001335147,0.0002337023,0.0003132393],"category_scores_gemma":[0.0001264314,0.0002484697,0.00002792635,0.001305547,0.0001346365,0.0004723466,0.00009407355,0.0001310967,0.0007284125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00022064,"about_ca_system_score_gemma":0.00001865559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005613128,"about_ca_topic_score_gemma":0.00006345664,"domain_scores_codex":[0.998296,0.00006940485,0.0004427225,0.0006548274,0.0002502727,0.0002867882],"domain_scores_gemma":[0.9992346,0.00005411371,0.0001870796,0.0003925962,0.00004463303,0.0000869331],"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":[0.00005265376,0.00002900755,0.009031157,0.00001417206,0.00001661955,0.000003906111,0.0008462758,0.003516557,0.9700946,0.003309327,0.0002799662,0.01280571],"study_design_scores_gemma":[0.001505145,0.0001708285,0.3680086,0.00002941303,0.0000544256,0.0002047222,0.001309625,0.05640261,0.5554053,0.01325694,0.002945419,0.0007069365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828484,0.00005043119,0.009003271,0.005827677,0.0001117629,0.0008238929,0.000004830792,0.0008730903,0.0004566676],"genre_scores_gemma":[0.9931539,0.00007023731,0.004605669,0.0001405835,0.00003074688,0.0001564125,0.0001805575,0.00002519008,0.001636685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4146893,"threshold_uncertainty_score":0.9999968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594930162567385,"score_gpt":0.2698393760911212,"score_spread":0.2538900744654473,"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."}}