{"id":"W2003803078","doi":"10.1016/j.watres.2013.10.007","title":"Pilot-scale investigation of drinking water ultrafiltration membrane fouling rates using advanced data analysis techniques","year":2013,"lang":"en","type":"article","venue":"Water Research","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Natural Sciences and Engineering Research Council","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fouling; Ultrafiltration (renal); Membrane fouling; Chemistry; Particulates; Water treatment; Biofilter; Organic matter; Natural organic matter; Membrane; Pulp and paper industry; Environmental chemistry; Chromatography; Environmental science; Environmental engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008409619,0.0005682189,0.0004976843,0.0002212942,0.0005264236,0.0004746511,0.0005544806,0.0006720895,0.0006773039],"category_scores_gemma":[0.001011999,0.0002427915,0.0005219068,0.0003367592,0.0003139048,0.0004801367,0.0003246413,0.000588732,0.0002109607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004321083,"about_ca_system_score_gemma":0.0004204187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004476658,"about_ca_topic_score_gemma":0.003421635,"domain_scores_codex":[0.9995976,0.00008356156,0.00004202244,0.0000787476,0.0001284359,0.00006961697],"domain_scores_gemma":[0.9993528,0.0002667401,0.00006178761,0.00007715935,0.0001823046,0.00005928884],"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":[0.001092229,0.001051209,0.00155653,0.0001140319,0.00002472067,0.00006477202,0.00005653283,0.0006043822,0.989059,0.00003124348,0.00008577044,0.006259587],"study_design_scores_gemma":[0.00008169452,0.003948675,0.004658866,0.000003261448,0.00004354527,0.00005769697,0.00003879832,0.001767623,0.9889961,0.00002534295,0.0003655851,0.00001285849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972959,0.0000653934,0.001972165,0.00002308501,0.000009975349,0.00009492587,0.0002200434,0.00004999389,0.0002685448],"genre_scores_gemma":[0.9940786,0.0001608127,0.004719059,0.00002827962,0.000005623041,0.00008576728,0.0002514042,0.00001401992,0.0006564351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004476658,"threshold_uncertainty_score":0.008901238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014579499460344,"score_gpt":0.3509732626346875,"score_spread":0.2495153126886531,"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."}}