Evaluation of response measures for water cutoffs using pressure driven analysis simulations
Bibliographic record
Abstract
Water distribution network (WDN) research rarely explores the effects of response measure configurations for water cutoffs. Furthermore, the research that does explore cutoff response measures quantifies performance with resilience metrics that are often difficult for water utilities to apply in operation. These factors hinder the subsequent exploration of tools that water utilities can use to find solutions for resilient water networks. Failure to address resilience in a WDN can lead to prolonged periods of water supply emergencies after failure events such as source-water contamination. Water supply emergencies are critical problems because they affect the welfare of communities and commercial operations. Thus, it is important for water utilities to have response measures that maintain adequate supply while full operation of the WDN is restored. This study analyzes the network-wide impact of response measures on a benchmark WDN undergoing a water cutoff scenario. Simulation is performed until supply is exhausted to explore the effect of the measures under pressure deficient conditions. The findings in this paper present a methodology that can be used to analyze response measures and aid water utilities in mitigation planning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".