Predicting Water Quality Impact After District Metered Area Implementation in a Full‐Scale Drinking Water Distribution System
Bibliographic record
Abstract
Pressure management using district metered areas (DMAs) can reduce leakage and break frequencies and extend the service life of pipes in drinking water networks. Valves must be closed, creating DMAs, resulting in hydraulic changes and increasing the number of dead ends. A field study of five pilot DMAs was conducted using an enhanced sampling program. Water quality was measured at different locations inside and outside DMA boundaries before and after implementation. Overall water quality did not change following DMA implementation. However, water quality (chlorine residuals, turbidity, and metals) was degraded at locations with elevated water residence times such as created dead ends, sites outside DMA boundaries, and extremities. An approach based on the combination of hydraulic modeling and water quality was developed to predict trihalomethane concentrations in the DMA using measurements only from an inlet site. Utilities can use a combination of hydraulic modeling and targeted monitoring to predict water quality changes after DMA implementation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".