A model for analyzing water reuse and resource recovery potential in urban areas
Bibliographic record
Abstract
Expansion and densification of urbanized areas can lead to increased pressure on an area’s water and wastewater infrastructure. This paper presents an optimization model using geographic information systems to locate potential sources and sinks of reclaimed water within an urbanized area. A mathematical solver then maximizes the water reuse using a set of constraints on mass balance, water quality, and electricity used for pumping. Three variations of the model are applied to Vancouver, British Columbia. The first scenario identifies a number of potential industrial sources and sinks for direct cascading with a total potential water savings of 133 400 m 3 /year. With the implementation of a satellite water reclamation facility in scenario 2, water recycling potential is increased to a maximum of 613 800 m 3 /year. Scenario 3 expands to include the non-industrial water users and the potential for water reuse is increased to a maximum of 969 200 m 3 /year.
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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".