Drinking Water Infrastructure Assessment: The National Research Council of Canada Perspective
Bibliographic record
Abstract
The essence of infrastructure asset management and decision-making on its renewal/rehabilitation is a trade-off between system performance and cost. System performance criteria for water networks include quality, quantity and reliability, i.e., the water should be safe, with acceptable aesthetics, taste and odour; regular and peak demand (including fire flows) should be met with acceptable pressure and with minimal interruptions. Costs comprise capital investment in system design, installation and renewal, operation and maintenance (energy, materials, labour, monitoring, inspection, testing, repair), and indirect and social costs incurred due to failure (property damage, disruption, illness, etc.). Several challenges need to be overcome in the development of an integrated decision framework for water distribution network. Mechanisms affecting system performance criteria are not all well understood. It is difficult to define and measure performance (which inherently comprises several non-commensurate and often conflicting criteria), let alone decide what level of performance is acceptable. It is also difficult to calculate the costs involved to achieve a specific level of performance. Substantial spatial and temporal variability is inherent in even a moderate-size network, and the collection of data on the performance and condition of these buried assets is often difficult and costly. At the National Research Council of Canada we have identified the need to address these issues in a holistic way, and in the last 15 years have been involved in a continual effort, both independently and in collaboration with others, to put the pieces of the puzzle together. Although the state of knowledge has advanced significantly since we started, a lot still needs to be achieved. This paper describes our past and current research activities, views and vision for future activities in the field.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".