Starting a Condition Assessment Program for PCCP in Tampa Bay Water's Wholesale System
Bibliographic record
Abstract
A condition assessment program should be an integral part of a utility's strategy for managing repair and replacement of its large-diameter piping. Using nondestructive condition assessment and performance analysis can prolong the useful life of piping, reduce the risk of catastrophic failure, allow for target rehabilitation, and significantly reduce the community disruption and capital cost of pipeline replacement projects. Tampa Bay Water is a wholesale water provider with more than $1 billion in assets and more than 100 miles of large-diameter piping. Tampa Bay Water's regional water delivery system supplies quality water to 2.3 million customers to six member governments: the cities of Tampa, St. Petersburg, New Port Richey, and Hillsborough, Pinellas, and Pasco Counties. The regional water system comprises groundwater and surface water sources, an off-stream storage reservoir, a seawater desalination plant and a collection of treatment facilities pipes and pumps. The focus of the agency has recently shifted from completing new water supply projects, to repairing and maintaining its large and diverse wholesale water treatment and conveyance system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".