Evaluation of fluorescence excitation–emission and LC-OCD as methods of detecting removal of NOM and DBP precursors by enhanced coagulation
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Bibliographic record
Abstract
Bench-scale tests were conducted to evaluate enhanced coagulation as a method for removing natural organic matter (NOM) from a surface water to reduce the formation of disinfection by-products (DBPs). Aluminium sulphate (alum) and two polyaluminium chloride (PACl) coagulants were used, as well as alum with pH depression. Using a PACl coagulant alone or alum with pH depression was shown to attain 35% removal of TOC at lower dosages (31 and 29 mg/L, respectively) when compared to the use of alum alone (43 mg/L). In addition to TOC and UV254, a fluorescence excitation–emission matrix (FEEM) approach and liquid chromatography–organic carbon detection (LC-OCD) were used to further characterize the removal of NOM in both untreated and filtered waters. Principal component analysis of FEEM was able to identify the presence of humic-like substances (HS), protein-like substances (PS), and colloidal/particulate matter (CPM); HS were found to have a close correlation with TOC and UV254. LC-OCD enabled the quantitative detection of hydrophobic and hydrophilic DOC; the latter was further separated into five components, the largest of which was HS. Strong linear correlations were calculated between TOC, UV254, HS, and hydrophilic DOC (r2 > 0.96); these parameters were also found to be closely correlated with the formation of trihalomethanes (THMs, r2 > 0.78) and haloacetic acids (HAAs, r2 > 0.92). Linear correlations with THMs and HAAs indicated that FEEM and LC-OCD provide good measures of DBP precursors when compared with TOC and UV254.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it