Negative Pressure Events in Water Distribution Systems: Public Health Risk Assessment Based on Transient Analysis Outputs
Bibliographic record
Abstract
Transient analysis of a pump trip was conducted on a full-scale distribution system (DS) equipped with high-speed pressure transient data loggers at the outlet of the water treatment plant (WTP) and at 12 DS sites. Following the calibration of the transient model (∼16,000 nodes) with transient pressure recordings, intrusion volume computations were performed considering two intrusion pathways: leakage orifices and submerged air vacuum valves (AVVs). As expected, the estimated intrusion volumes through submerged AWs are considerably larger than those through leakage orifices. Water quality modeling was conducted in order to evaluate the spatiotemporal dispersion of the intruded water, assumed to be contaminated with Cryptosporidium oocysts. A point estimate of the maximum probability of infection was then computed at each DS node using the negative exponential model. The estimated maximum probabilities of infection were displayed on the DS map and the model assumptions are discussed. This exercise has underlined important risk gradients and the localized occurrence of very high probabilitiesof infection, suggesting that a global risk analysis might be misleading. This project is the first attempt at quantifying public health risks induced from low pressure events in a large scale system (supplying ∼400,000 people) based on actual negative pressure recordings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".