{"id":"W2896684188","doi":"10.1051/e3sconf/20185900001","title":"Membrane surface morphology and fouling in filtration of high DOC water","year":2018,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Nanofiltration; Ultrafiltration (renal); Fouling; Microfiltration; Membrane; Chemistry; Filtration (mathematics); Dissolved organic carbon; Membrane fouling; Chlorine; Surface water; Chromatography; Environmental chemistry; Chemical engineering; Environmental engineering; Environmental science; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001582253,0.0002191249,0.0001819104,0.0003274575,0.0008695522,0.0006203495,0.0002290306,0.0002818542,0.000574958],"category_scores_gemma":[0.0002767921,0.0001616805,0.0001844072,0.0003733576,0.0003049881,0.0001807757,0.0001434315,0.0002555263,0.0001609325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002913315,"about_ca_system_score_gemma":0.00128516,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5133303,"about_ca_topic_score_gemma":0.508499,"domain_scores_codex":[0.9997883,0.000009000179,0.000007761873,0.00004099247,0.0001047584,0.00004921747],"domain_scores_gemma":[0.9998531,0.00001513864,0.0000223317,0.000004609959,0.00008227641,0.00002252966],"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":[0.00007782714,0.00002516964,0.009254687,0.00003872324,0.000007360136,0.0001057328,0.0002445515,0.0002526373,0.9872995,0.00004493829,0.00006569271,0.002583158],"study_design_scores_gemma":[0.000007421841,0.0003414947,0.2761458,0.00001147531,0.00002514181,0.0002851958,0.0007505232,0.001611693,0.7180375,0.00003768464,0.00272009,0.00002597347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998544,0.0001591091,0.0003495729,0.00001343889,0.000002621778,0.00001164395,0.0001476591,0.0000103475,0.0007617268],"genre_scores_gemma":[0.9954048,0.0002702631,0.001339143,0.00002505419,0.000001059041,0.00001033997,0.0002856546,0.000009096535,0.002654434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5133303,"threshold_uncertainty_score":0.9790715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893509360482055,"score_gpt":0.2469076936414565,"score_spread":0.2279726000366359,"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."}}