{"id":"W1983948558","doi":"10.1016/j.seppur.2013.12.003","title":"Nanofiltration of pharmaceutically active and endocrine disrupting compounds as a function of compound interactions with DOM fractions and cations in natural water","year":2013,"lang":"en","type":"article","venue":"Separation and Purification Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Nanofiltration; Chemistry; Ultrafiltration (renal); Colloid; Organic matter; Membrane; Natural organic matter; Filtration (mathematics); Dissolved organic carbon; Environmental chemistry; Fractionation; Size-exclusion chromatography; Humic acid; Chromatography; 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":[],"consensus_categories":[],"category_scores_codex":[0.0000833446,0.0001213421,0.0001691799,0.000305615,0.0001421531,0.00002859534,0.00006748256,0.00008862119,0.0001877723],"category_scores_gemma":[0.00004762013,0.00009848936,0.0000116921,0.0003715141,0.0005634172,0.0006592062,0.00005420699,0.0001920687,0.00001858687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004214988,"about_ca_system_score_gemma":0.00001130982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002319492,"about_ca_topic_score_gemma":0.0003677368,"domain_scores_codex":[0.9990731,0.00004620384,0.0003556453,0.0002725815,0.0001191918,0.0001332954],"domain_scores_gemma":[0.9994529,0.00008172564,0.0001890781,0.0001734562,0.00007066602,0.00003215597],"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":[0.00007279793,0.000111191,0.0107211,0.00002212807,0.00002154647,3.045822e-7,0.0007045141,0.0000717958,0.9603558,0.008996055,0.00005014309,0.01887259],"study_design_scores_gemma":[0.001547782,0.0004802435,0.188913,0.00004447041,0.00007310476,0.0001698026,0.005081654,0.007960073,0.7790176,0.01183958,0.004522435,0.0003502721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929464,0.00005605501,0.00176342,0.00320743,0.00003839364,0.0005393017,0.0000045203,0.00007477034,0.00136969],"genre_scores_gemma":[0.9969699,0.0001120103,0.002495718,0.00004070996,0.000004793893,0.0001485185,0.00004630113,0.000007020657,0.0001750211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1813383,"threshold_uncertainty_score":0.401628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132565879733623,"score_gpt":0.2895572556338248,"score_spread":0.2782315968364886,"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."}}