Revised Chlorine Mass Balance for Chlorine Loss Assessment in Water Distribution Networks
Bibliographic record
Abstract
Water and energy balances in water distribution networks (WDNs) are commonly used for managing water and energy losses, respectively. Recently, a new approach, the chlorine mass balance, has been proposed to assess chlorine losses within WDNs. However, previous research did not account for changes in chlorine masses in pipes and tanks within the networks (∆MN). In this study, we introduce ∆MN as a new component in the revised chlorine mass balance and assess its significance by utilizing a simple WDN model with a downstream tank. Our findings reveal that the hourly magnitude of ∆MN can be comparable to the other two primary components: the chlorine mass delivered to users, and chlorine mass losses by reactions. This underscores the importance of ∆MN for the short-term assessments, particularly in cases involving intermittent water supply and pressure-loss events. During non-supply periods, chlorine concentrations in stagnant water within pipes and tanks decrease due to reactions, resulting in negative ∆MN. When water supply resumes, a portion of the input chlorine mass is used to restore chlorine levels in WDNs, resulting in positive ∆MN. ∆MN fluctuates between positive and negative values with an average value around zero in continuously operating general WDNs. Therefore, if the balance is assessed over a long period with many cycles of periodic patterns, ∆MN becomes less significant.
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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 | 0.000 | 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 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".