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.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".