WATER REUSE IN CANADA: OPPORTUNITIES AND CHALLENGES
Bibliographic record
Abstract
Reclamation and reuse of various types of wastewater, including stormwater, greywater, and domestic wastewater, represents an important component of the urban water cycle helping close the loop between water supply and wastewater disposal. Safe and scientifically-based water and wastewater reuse has been practised for about a century, and a great wealth of practical experience with such practices has been reported in the literature. Essential elements of water reuse plans include the selection of categories of reuse, selection of water quality criteria for such specific reuses (in accordance with the existing regulations and guidelines), design of the treatment train providing the effluent of the required quality, and examination of overall feasibility. In Canada, water reuse is generally conducted on a small-scale or experimental basis. While no national guidelines exist at this time, a number of provinces have developed guidelines for specific water reuse applications. The current stresses on water supply, caused by growing population and increasing water demands, depletion of water sources, reduced supply reliability caused by climate change, ageing infrastructure and limited funding for its expansion, as well as the promotion of environmental sustainability and needs to reduce wastewater discharges to sensitive receiving waters, will contribute to further growth and expansion of water and wastewater reclamation and reuse.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".