Development Of A Guideline For Integrating Municipal Infrastructure Asset Management With Wastewater Energy Recovery Systems
Bibliographic record
Abstract
<div>Wastewater energy recovery systems (WWERS) cycle residual heat from sewers back into a space for temperature conditioning. Using recovered energy instead of fossil fuels is a sensible direction towards a circular economy. Existing literature, while rich in technical considerations, does not analyze the decision-making process related to the wastewater infrastructure changes. Therefore, the purpose of this research was to bridge this gap in the literature through the development of a planning guideline, targeted to municipal owners of wastewater infrastructure. The proposed planning guideline was then applied to the Regional Municipality of York, a two-tier municipality in Ontario, Canada as a case study. The case study demonstrated the efficacy of the guideline, using publicly available municipal data to discern feasibility of centralized WWERS. Results may aid municipalities or WWERS proponents in advancing to a more widespread use, as an effective first step in bridging academic literature with often-stated municipal goals of increased sustainability of infrastructure systems. </div>
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".