AC Enigma: Using Opportunistic Sampling to Assess Asbestos-Cement Water Main Performance in Winnipeg
Bibliographic record
Abstract
With a population of over 680,000 people, the City of Winnipeg is the eighth largest city in Canada. To support this user base the City maintains an inventory of over 2,900 km of water mains, consisting of cast iron (∼30 %), asbestos cement (∼27 %), PVC (∼33 %), and prestressed concrete pressure pipe (∼5 %). Historically, the City focused its investment on the replacement and cathodic protection of cast iron mains, which due to highly corrosive soils had experienced extreme deterioration. Consequently, very little was known about the condition of its remaining system, leaving the City exposed to risk and uncertainty over coming planning horizons. Wishing to improve its understanding of material performance and ability to target current and future water system investments, the City worked with UMA Engineering Ltd. to undertake a Water Main Criticality Assessment Study. One component of this project was to undertake an assessment of the City's asbestos-cement (AC) water main inventory. Winnipeg's first AC water mains were installed in 1932, at a time when AC pipe was being introduced to North American markets. Early studies found that they performed well in Winnipeg's aggressive soils, which resulted in widespread adoption and use of AC within the city until the early 1980s. While some work was done to assess the impact of water chemistry on internal pipe wall degradation, very little was known about the long-term performance of the lines, and the risk and liability they might pose. Seeing to address these concerns, an AC Sampling and Testing Program was developed and introduced. Under the AC Sampling and Testing Program, pipe segments were collected opportunistically from in-service water mains throughout the city and subjected to an array of destructive and non-destructive test procedures. Data was collected and analyzed to assess the current risk posed by the City's AC inventory, and to extrapolate long-range system performance. This paper reviews techniques used in the collection and testing of pipe samples, presents findings from the analysis of current and long-term performance, and discusses the challenges faced in introducing a standardized AC sample collection and testing program in the City of Winnipeg.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".