{"id":"W4233311608","doi":"10.4018/978-1-7998-1210-4.ch033","title":"Removal of Emerging Contaminants from Water and Wastewater Using Nanofiltration Technology","year":2019,"lang":"en","type":"book-chapter","venue":"Waste Management","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nanofiltration; Filtration (mathematics); Wastewater; Effluent; Contamination; Environmental science; Membrane technology; Water treatment; Water quality; Waste management; Ultrafiltration (renal); Environmental engineering; Membrane; Chemistry; Chromatography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007376277,0.0004572431,0.0003546138,0.0006238903,0.0002111498,0.0006262649,0.0003378528,0.0004542278,0.003275441],"category_scores_gemma":[0.00005760175,0.0001658502,0.0004796755,0.0006392886,0.0001178241,0.000864687,0.0003311694,0.0006689261,0.002494097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003281438,"about_ca_system_score_gemma":0.0002562653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004244755,"about_ca_topic_score_gemma":0.0009781828,"domain_scores_codex":[0.9999241,0.000002896528,0.000003877181,0.0000151907,0.00004672063,0.000007212089],"domain_scores_gemma":[0.9999902,0.000002506933,0.000001435117,6.039497e-7,0.00000411535,0.000001116978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004150883,0.0001335701,0.0001339373,0.002780038,0.0000235902,0.0003094552,0.0001909581,0.00142576,0.4666848,0.01284354,0.01482207,0.5006109],"study_design_scores_gemma":[0.000008710181,0.000249839,0.0009139014,0.0004627919,0.0000401924,0.001038337,0.00007173404,0.002535765,0.1831705,0.004451017,0.8070216,0.00003548658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.09349626,0.4899524,0.1168297,0.00230652,0.002717868,0.0003193065,0.001551806,0.001532403,0.2912939],"genre_scores_gemma":[0.1220441,0.4049512,0.1049021,0.001173525,0.000533935,0.0001961001,0.002238029,0.0002594789,0.3637016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003275441,"threshold_uncertainty_score":0.01095748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433822369268557,"score_gpt":0.2179033225907046,"score_spread":0.203565098898019,"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."}}