Bibliographic record
Abstract
There is growing awareness in the water industry of the magnitude of transmission main water loss. Additionally, there is a tremendous opportunity to improve water efficiency by managing this water loss effectively. The IWA standard Water Audit framework represents decades of effort in establishing best practices for water loss management, and has now been adopted by the AWWA as well, making this a globally recognized standard. This framework, however, has largely ignored transmission mains, on the assumption that they rarely leak. This paper presents aggregate data representing all in-line leak location surveys on large diameter water mains conducted to-date, by all providers worldwide. This data spans over 2,000 miles of inspections conducted by tethered acoustic devices, and tells a very different story of transmission mains, demonstrating that actual transmission main leakage is close to 100 times the levels previously assumed. Breakdowns of the data are provided by various predictive factors, such as pipe age and material. Case studies of water loss management programs are presented from cities around the world. A cost / benefits model for transmission main leakage control is presented, and applied to these projects. The results of this analysis show that transmission main leakage offers an economical opportunity for water efficiency gains, making it a key component of water loss control programs.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".