Advancing Water Network Sustainability through Smart Utility Approaches: Case Studies and Lessons Learned (WRF 4917)
Bibliographic record
Abstract
This study delves into the transformative potential of Smart Water Network (SWN) technologies and their pivotal role in mitigating water main breaks and leaks. A smart utility approach emerges as a tool in managing pressure and flow dynamics within distribution systems by integrating real-time data from sensors with advanced information and communication technologies. The project, sponsored by the Water Research Foundation (WRF), recently culminated in SWN pilots across a spectrum of utility sizes. These case studies, conducted in both large and small utilities, not only validated the efficacy of SWN technologies, but also provided a wealth of invaluable lessons and insights. This paper will highlight detailed case studies from four utilities involved in the WRF project, namely, the City of Lakewood, California; the Water and Wastewater Authority of Wilson County, Tennessee; Sydney Water in Sydney, Australia; and the Water Corporation of Western Australia in Perth, Australia.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".