Benchmark Performance Indicators for Utility Water and Wastewater Pipelines Infrastructure
Bibliographic record
Abstract
Over the past decade, many performance indicators have been developed for water utilities to track their system performance. This study proposes a set of normalized and time-integrated benchmarking performance indicators for sustainable long-term management of water distribution and wastewater collection networks. The benchmarking performance indicators are aggregated into three categories: (1) infrastructure, (2) sociopolitical, and (3) financial. To demonstrate the use and value of the benchmarking performance indicators, a system dynamics model is used to present a case study for three water utilities in southern Ontario, Canada. This study shows that the benchmarking performance indicators will allow water utilities with different attributes (such as number of customers, network pipe age profile, pipe material type, network size, and location) to benchmark the long-term variation in their performance with other utilities regionally and nationally. Furthermore, the benchmarking performance indicators can be used to forecast the future behavior of the system to ensure decision-making policies that will drive improvements and best practices.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".