Energy Metrics to Evaluate the Energy Use and Performance of Water Main Assets
Bibliographic record
Abstract
Managing aging infrastructure has become one of the greatest challenges for water utilities, particularly when faced with selecting the most critical pipes for rehabilitation from among the thousands of candidates. This paper presents a set of novel yet practical energy metrics that quantify energy interactions at the spatial resolution of individual water mains to help utilities identify pipes for rehabilitation. The metrics are demonstrated using a benchmark system and two large, complex systems. The results show that the majority of pipes have good energy performance but that an important minority of outlier pipes have low energy efficiency and high energy losses due to friction and leakage. Pumping and tank operations tend to drive energy efficiency and energy losses in pipes close to water sources, whereas diurnal variation in demand drives energy performance of mains located far away from water sources. The new metrics of energy lost to friction and energy lost to leakage can provide information on energy performance in a pipe that is complementary to the traditional measures of unit head loss and leakage flow.
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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".