A Review of Water Reuse and Recycling, with Reference to Canadian Practice and Potential: 2. Applications
Bibliographic record
Abstract
Abstract Common water reuse applications include agricultural and landscape irrigation with treated municipal wastewater, industrial recirculation of process waters, rainwater collection, and groundwater recharge for non-potable and indirect potable reuse. As compared to other countries worldwide, water reuse is currently practised infrequently in Canada, with the focus of most of the water reuse effort within Canada on agricultural irrigation applications. Landscape irrigation and other non-potable urban uses are practised to some extent, but provide an opportunity for expanded application of reclaimed water. Similarly, while water recycling is practised to various degrees within specific industrial sectors, further industrial water reuse and recycling affords an opportunity to conserve large volumes of water. The Canada Mortgage and Housing Corporation (CMHC) has supported a great deal of research into treatment and reuse of domestic greywater for non-potable uses within individual buildings, as well as some work on rainwater collection and use. Groundwater recharge and potable reuse are practised to some extent in extremely dry regions of the world, but public health concerns with respect to emerging trace contaminants may limit the spread of these reuse applications. The main issues associated with each of the above applications are reviewed, and the state of Canadian water reuse and recycling is described.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".