Best Practices in the Condition Assessment of Water Transmission Mains
Bibliographic record
Abstract
Large diameter water transmission mains represent a significant portion of the underground assets of any given water utility. Until recently, there have been few options available for ascertaining the actual condition of these assets. One notable exception has been Prestressed Concrete Cylinder Pipe (PCCP). Since the invention of RFTC technology, developed at Queen's University in Kingston, Ontario, Canada in the early 1990's, more than 4,000km of PCCP has been assessed. This condition based asset management technique has become the standard for those utilities who want to establish a long term maintenance and management plan for their PCCP networks. With the introduction of the Sahara leak detection system, the industry now has a powerful tool to gather direct information about the condition of any transmission main — regardless of its material construction type. The Sahara system accurately pinpoints the location and size of leaks as small as 1 liter/hour. A utility can utilize this system to precisely locate leaks, reduce non-revenue water, identify leaks causing pipeline commissioning delays, and as a condition assessment / risk management / asset validation tool that provides information that forms the basis of future asset management strategies.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".