Watermain Asset Management
Bibliographic record
Abstract
York Region (the Region) is one of the fastest growing communities in the Greater Toronto Area. It operates a “two tier” level of government, taking responsibility for the large diameter trunk water mains and sewers serving the population. The lower tier municipalities take responsibility for local distribution and collection systems. The Region owns and operates approximately 350 km of large diameter watermains which transport water from treatment facilities to the local distribution system. The pipe material inventory is predominantly concrete pressure pipe (AWWA C301) at 83%, with the next largest material group being ductile iron (DI) at 9% and PVC & HDPE make up 7% of the inventory. The system is relatively young in age, with 72% of its entire inventory being less than 20 years of age, and 27% being 20 to 40 years of age. The diameter of the Region’s watermain inventory ranges from 1800 to 400 mm with an average diameter of approximately 750 mm. After early adoption of field investigations of concrete pressure pipes using leading edge condition assessment technologies including wet, live deployed electromagnetic inspection technologies along with confirmatory forensic exhumations, the Region took a step back to evaluate the effectiveness of its tactic and subsequently determined to take a more strategic, risk-based and holistic approach to its watermain asset management. The Region also collaborated with other municipalities in North America to identify industry best practices. A gap analysis was then undertaken to identify a road map of actions and timeframes to better target future condition assessment activities. It was determined that a few key exercises are best to be completed in advance of field works. As a summary those tasks include: sorting watermain in descending priority sequence according to risk, to think through the potential results of condition assessment and subsequent resulting actions in advance of the field works and to better plan and prepare for contingencies and probable outcomes. By better understanding the inspection and condition assessment tools and their suitable uses and likely results, the Region has a clearer and more fulsome understanding of how to manage risk while sustaining this critical infrastructure at the lowest overall lifecycle cost.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".