Infrastructure Asset Management Readiness Assessment of Ontario Municipal Water Utilities
Bibliographic record
Abstract
The safety and quality of services provided by municipal water utilities depend on sound water and wastewater infrastructure. Nevertheless, according to the 2016 Canadian Infrastructure Report Card, about 12% of this infrastructure is in poor or very poor condition, representing CA$51 billion in asset replacement value. Infrastructure Asset Management (IAM) is a strategic approach that encourages municipalities to take into account long-term analysis to set priorities for asset-related decisions. The Province of Ontario has developed regulations and guidelines to broadly implement municipal Asset Management Plans (AMPs). However, the existence of an AMP does not guarantee reliable IAM. For assets to be properly managed, water utilities must have processes in place to base decisions on technical and financial information, that consider assets’ life cycle and levels of service. The adoption of IAM processes can be measured by a readiness assessment. The main purpose of this study is to assess the current asset management readiness level of Ontario municipal water utilities, while providing direction and support for the development of policies and guidelines. Additionally, it investigates whether AMPs are sources of information for evidence-based decision-making. The Federation of Canadian Municipalities’ Asset Management Readiness Scale, the ISO 5000 series, and the Ontario Regulation 588/17 were adapted and used as a framework for a voluntary web-based survey. Data was provided by 31 municipalities representing 51% of the Ontario population. Respondents are classified into four readiness levels (RLs) – RL 1, RL 2, RL 3, and RL 4 – according to five competency areas: (1) policy and governance; (2) people and leadership; (3) data and information; (4) planning and decision-making; and (5) contribution to asset management practice. Readiness level results varied between 0.17 and 1.18 for small, medium and large municipalities, on a scale of 0 to 3. Additional results provide insights regarding levels of service, communication of key IAM information, funding gaps, service fees, and climate change aspects considered in asset management planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".