{"id":"W2044717702","doi":"10.2166/ws.2008.095","title":"Assessing nanofiltration fouling in drinking water treatment using fluorescence fingerprinting and LC-OCD analyses","year":2008,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","funders":"","keywords":"Nanofiltration; Fouling; Chemistry; Natural organic matter; Fluorescence spectroscopy; Membrane fouling; Chromatography; Organic matter; Membrane; Water treatment; Humic acid; Fluorescence; Environmental chemistry; Environmental science; Organic chemistry; Environmental engineering; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009385201,0.0003413784,0.0003895373,0.0009593854,0.001334093,0.0003261268,0.0005842432,0.0001710031,0.00006823612],"category_scores_gemma":[0.00002103559,0.0001937292,0.00007512055,0.0008520939,0.001631046,0.002181515,0.0007443387,0.0002310825,0.00008809182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004819923,"about_ca_system_score_gemma":0.00002193377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786278,"about_ca_topic_score_gemma":0.00003599058,"domain_scores_codex":[0.9965366,0.00008416147,0.0005445745,0.001063875,0.0005050007,0.001265752],"domain_scores_gemma":[0.9992613,0.00001606879,0.0000627242,0.0005147974,0.00003059448,0.0001145559],"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":[0.000002606997,0.000038916,0.2756225,0.000003458004,0.000006136028,0.0000583896,0.003848569,0.001440534,0.717169,0.000003083933,2.892636e-7,0.001806429],"study_design_scores_gemma":[0.0002519245,0.00005575461,0.006178449,0.00004555946,0.00002919258,0.0001221725,0.0005719943,0.006784475,0.9851291,0.0004060368,0.0000844434,0.0003409303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973856,0.00001720605,0.001093318,0.0009740476,0.0001483036,0.0001524929,3.902859e-7,0.0001658428,0.00006275371],"genre_scores_gemma":[0.9867988,0.00001711573,0.01295701,0.00002829346,0.00004462946,0.0000216032,0.000006876033,0.00001981792,0.0001058995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2694441,"threshold_uncertainty_score":0.999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06262330841437676,"score_gpt":0.3206137588550173,"score_spread":0.2579904504406405,"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."}}