Tucson Water's Homegrown Condition Assessment of PCCP
Bibliographic record
Abstract
On February 5, 1999, a 96-in. diameter pipeline failed catastrophically, funneling 38 million gallons of water through a half-mile square residential area in just 90 minutes, causing extensive property damage and resulting in 12 homes being condemned. The City vowed to the community that it would begin performing annual inspections of this and similar pipelines. Within months of the catastrophic failure, Tucson began networking with other agencies that operated and maintained similar pipeline infrastructure. By February 2000, Tucson implemented its own condition assessment program, named the Pipeline Protection Program (PPP), and it evolved to become one of the more advanced predictive and preventive maintenance programs for prestressed concrete cylinder pope (PCCP). Beginning with routine internal visual pipeline inspections, internal electromagnetic surveys, and hydrophone arrays and quickly advancing to acoustic fiber optics (AFO), Tucson has managed to avoid any catastrophic failures, yet had a very close call when it identified a recent incipient failure of a 96-in. pipeline. Alerted by Tucson's AFO system, the 96-in. pipeline had all the potential to surpass the damage and destruction experienced in the $5 million 1999 failure. This technical paper shares some of the biggest challenges faced when building and implementing a condition assessment program for large-diameter pipelines and highlights successes, including how Tucson's most recent investment in AFO paid off big for Tucson.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.014 | 0.002 |
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".