The Water Utility Risk Integration Matrix: Demonstrating Potential for an Integrated Approach to Municipal Water Management
Bibliographic record
Abstract
Municipal water utilities traditionally have been managed in isolation with little consideration of common factors affecting risk.An integrated approach to municipal water management has great potential for increasing the resilience of these systems under future uncertainties.In a recent survey conducted by the University of Guelph, respondents from various Canadian municipalities identified aging infrastructure, climate change and urbanization as the top three drivers of risk to water infrastructure.To demonstrate the potential for an integrated approach to risk management of water infrastructure, the Water Utility Risk Integration Matrix document was developed.This interactive document emphasizes commonalities across municipal water sectors and allows users to quickly access relevant information on the previously identified drivers of risk.Each chapter provides an overview of the impact each driver has on water treatment and distribution, sewage conveyance and treatment, combined sewer systems, and stormwater conveyance.The document provides direct access to additional details and external webpages containing statistics and examples for any implications of interest to the user.Relevant guidance and case studies are also incorporated into the document with links to external data sources.This document facilitates information gathering to assist in the widespread adoption of an integrated approach to risk management of municipal water utilities and provides a framework for further development of risk management initiatives.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.001 |
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".