Understanding Risk and Resilience to Better Manage Water Transmission Systems
Bibliographic record
Abstract
The Regional Municipality of Peel, Canada (the Region), a suburb of Toronto, through its growth projections will be tasked with supplying over 2.5 million residential and commercial customers with drinking water over the next twenty years. In response to this, the Region has undertaken a review of its transmission and sub-transmission infrastructure to ensure it can continue to deliver drinking water services that meet its customer’s needs. This project consists of undertaking a risk and resilience assessment in order to understand and proactively manage threats and opportunities to key components of the Region’s water distribution system and to ensure continued and reliable delivery of water service to its customers. The key focus for the project was to develop a long term strategy to manage and reduce risk through capital improvements and operational planning. In addition, it was necessary to link corporate asset management objectives to risk impacts and resilience enhancements to ensure they are translated into, and support the appropriate business planning processes and life cycle management strategies for the critical assets. The AWWA 7-Step RAMCAP risk management process was used to meet the overall project objectives. Existing and planned protective measures were used to generate alternative options for managing critical asset risks. Risk mitigation options included repair, rehabilitation, replacement, adding redundancy and other operational procedures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".