Application of Energy Use Indicators to Evaluate Energy Dynamics in Canadian Water Distribution Systems
Why this work is in the frame
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Bibliographic record
Abstract
Over the past 14 years, researchers have been developing energy indicators to better quantify where energy is being wasted in relation to the operation of a water distribution system. These energy indicators have helped municipalities identify ways to make their systems more energy efficient. This paper builds upon research completed by Cabrera et al. [4] where five (5) energy indicators were developed. The indicators strive to describe the efficiency of a distribution network by focusing on what is causing the energy consumption within a system (i.e. friction, leakage etc.). In this paper these indicators are applied to three (3) distribution networks. The energy use indicators are used to compare energy efficiency, energy lost to friction and leakage, and useful energy between each of the modelled distribution networks. This paper also discusses the key characteristics of the networks that account for the energy use patterns observed with the energy indicators.
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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