Building the case for water and resource recovery in Canada: practitioners' perspectives
Bibliographic record
Abstract
Abstract Water and resource recovery (WRR) involves the collection and treatment of rainwater, stormwater, and/or municipal wastewater to a fit-for-purpose standard. There is no national policy for WRR in Canada, and there are minimal WRR-specific provincial regulations; given this lack of regulation, current projects are highly specific to the local context and approved individually. We engaged people who work with water and wastewater services in the province of Alberta, Canada to discuss what WRR could look like in their context. During 3-h workshops, information on WRR was shared and participants engaged in discussions using a World Café process. Participants discussed the need for supportive regulations and government leadership, financial support, collaboration and knowledge sharing, education and communication, and accounting for risk and liability. Given that the participants are individuals who would be impacted by the development of regulations for WRR, we discuss concepts to provide the guidance needed for the successful implementation of WRR. This research connected experts in water and wastewater and gave space for developing ideas that make sense to those most closely involved in delivering WRR systems.
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.050 | 0.025 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".