Water Loss Levels from Transmission Mains in Urban Environments
Bibliographic record
Abstract
As aging water infrastructure and increasing water supply pressures become a fact of life in North American cities, attitudes towards leakage from water mains are changing. Water audits are becoming part of the standard operation of water utilities, and the volumes of treated water escaping from pipelines are coming under increased scrutiny. Most urban utilities now have leak detection programs to reduce leakage from their small diameter distribution mains. Until recently, however, the large diameter transmission mains have been largely overlooked, due to a lack of concrete data on how much water loss originates in these pipelines, and a lack of effective technologies for locating all leaks on such lines. New technologies engineered specifically for leak detection in large diameter water mains have now emerged on the market, and empirical data is available on actual leakage rates from large diameter mains in urban environments. This paper combines results obtained in Dallas, Philadelphia, Allentown, Montreal, and Toronto, and presents a discussion of the number of leaks, their volume, and their distribution, as well as a comparison to the expected results. Cost / benefit analysis will be presented as well when possible.
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 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 | 0.000 | 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".