{"id":"W2344750924","doi":"10.2175/106143016x14504669767931","title":"Wastewater Colloidal Organic Carbon: Characterization of Filtration Fractions Using <sup>1</sup>H NMR","year":2016,"lang":"en","type":"article","venue":"Water Environment Research","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wastewater; Ultrafiltration (renal); Chemistry; Filtration (mathematics); Characterization (materials science); Chromatography; Colloid; Size-exclusion chromatography; Carbon-13 NMR; Absorbance; Analytical Chemistry (journal); Nuclear chemistry; Materials science; Organic chemistry; Nanotechnology; Environmental engineering; Environmental science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000262768,0.0001238724,0.0001304225,0.0001516715,0.0001193779,0.00002147407,0.0001422544,0.00009853401,0.001665638],"category_scores_gemma":[0.00000380834,0.00008338899,0.00003591519,0.000124173,0.0001006222,0.0001277501,0.00005931491,0.0001346082,0.000187466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002662615,"about_ca_system_score_gemma":0.00001659484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000154491,"about_ca_topic_score_gemma":4.509917e-7,"domain_scores_codex":[0.9986798,0.00008033756,0.0002577572,0.0002193463,0.0003648673,0.000397888],"domain_scores_gemma":[0.9995066,0.0000248004,0.00001813174,0.0003497189,0.00002821768,0.00007253102],"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":[0.000008339768,0.00003575832,0.0003734252,0.00001814516,0.00002645442,8.362211e-7,0.0002444402,0.000240613,0.9985015,0.00003051655,0.0002562057,0.0002637123],"study_design_scores_gemma":[0.0001856273,0.00004173256,0.0006266455,0.0000150022,0.00001087796,0.000004856482,0.00004302032,0.001811111,0.9806448,0.00007023673,0.01643117,0.0001148847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995313,0.000203256,0.003695851,0.0001901625,0.00001528763,0.0003027017,0.00001930162,0.00004160303,0.0002188702],"genre_scores_gemma":[0.9927222,0.00581842,0.00003383817,0.000004459893,0.00009483728,0.0000687014,0.00006757643,0.00004045124,0.001149566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01785672,"threshold_uncertainty_score":0.999247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771497431218324,"score_gpt":0.229571825741103,"score_spread":0.2118568514289198,"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."}}