Failure conditions of asbestos cement water mains in Regina
Bibliographic record
Abstract
Approximately two thirds of the water mains in the City of Regina are asbestos cement (AC) pipes. These pipes are experiencing more and more failures in recent years and account for almost all of the water main breaks in the city. The AC water main failures, along with the failure of other types of water mains in general, are a result of various factors, some of which are site specific. To assess the condition and identify the factors that significantly influence the breakage of AC water mains, the historical failure data for AC water mains in the City of Regina have been collected, along with their corresponding working environments, including soil type, water quality, weather, etc. The AC water main break data were analyzed for correlation between pipe breakage and all known physical, environmental and operational factors. The predominant factors that influence the AC pipe breaks were identified. It was observed that pipe age, diameter, climate, soil and construction and repair methods all influence the condition of the AC water mains in Regina, with climate and soil conditions being the two critical factors. Water quality is not a major determinant of the AC water main condition for the city. The observations will serve as a basis for further research on the failure mechanisms of the AC pipes, which is essential for the performance prediction of these water mains and, therefore, the management of the city's AC water main assets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".