Benchmark Performance Indicators for Utility Water and Wastewater Pipelines Infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it