Variations of DBPs precursors according to location within the distribution system
Bibliographic record
Abstract
Little information is available on the evolution of remaining organic matter (ROM) in a water distribution system (WDS) and its impact on the generation of disinfection by-products (DBPs). This research involves the characterization, through sample fractionation processes and experimental chlorination tests, of the reactivity of DBP precursors occurring within a WDS. The study is based on samples collected in various locations of a WDS during a complete year. For each sample, six fractions were generated to determine their potential for formation of trihalomethanes (THMs) and haloacetic acids (HAAs). Fractionation processes on ROM demonstrated that the spatial behavior of precursors for THMs differs from that for precursors of HAAs. In addition, experimental chlorination tests showed that the reactivity of the investigated fractions, in terms of DBP formation potential (DBPfp), was different from each other according to location in the WDS. DBPfp for the studied fractions changed drastically during water treatment. However, changes of DBPfp for fractions were relatively low between the beginning and the extremity of the distribution system. Since the results of this research confirm that the ability to produce DBPs is related to the nature of the fractions, they could be useful to evaluate the impact of re-chlorination on DBP formation in a WDS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".