Tacoma's Pipeline Assessment Project: Replacing the Right Mains at the Right Time
Bibliographic record
Abstract
Tacoma Water is a public water utility serving approximately 300,000 people in Tacoma, Washington, and neighboring communities. Founded in 1893, Tacoma Water owns and operates more than 1,200 mi of distribution water mains with more than 95,000 service connections. Like many water utilities, Tacoma Water faces the challenge of a limited capital budget and aging infrastructure. To compound this challenge, Tacoma Water has acquired multiple smaller water systems beyond its original borders with little known service history, making their useful life more difficult to assess. As a means of bridging the gap between available capital funds and the capital requirements of replacing aging mains, Tacoma Water has conducted two pipeline condition assessment projects to ensure that these limited capital funds are spent where they are most needed. In 2011 and 2013, Tacoma Water assessed 19 and 12 miles of distribution mains using an acoustic method for measuring the average remaining structural wall thickness of water mains. This method is fully nondisruptive, requiring no insertion of sensors into the mains, and no interruption of service for customers. These results were used to calculate the remaining useful life of each of the mains, which guided the prioritization of main replacement projects. This paper provides details of the Tacoma Water condition assessment projects, the technology used, the benefits of performing condition assessment, and how this has shaped the pipe replacement decision process in Tacoma Water.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".