Identifying key pipe attributes and locations to best determine chlorine decay coefficients within a water distribution system
Bibliographic record
Abstract
Abstract Water distribution system (WDS) characteristics can impact drinking water quality. Kinetic reaction coefficients of residual disinfectant associated with bulk water (kb) and pipe wall (kw) during water distribution can lead to water quality degradation. Determining these coefficients can be expensive and time-consuming. The main objective of this study is to determine the most relevant pipe characteristics and locations to best determine chlorine decay coefficients in a WDS. This article aims to evaluate various scenarios of kb and kw values and compare them to measured data in a full-scale WDS. The most accurate scenario is also compared with the lowest-cost scenario to identify the most effective information needed to determine these coefficients, in terms of location within the WDS and pipe characteristics (age, diameter, and material). Results showed that the scenario with the highest kw and kb values corresponds best to the field-measured data. Moreover, determining specific kinetic coefficients was shown to be more accurate for gray cast iron pipes, pipes installed in a period before 1960, and vulnerable zones for residual chlorine decay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".