Developing Capacity for Large-Scale Rainwater Harvesting in Canada
Bibliographic record
Abstract
Abstract Rainwater harvesting (RWH) is the ancient practice of capturing rainwater from impervious surfaces and storing it for future use. Harvesting roof runoff for domestic purposes has historically been prevalent in rural areas of Canada and the practice is currently experiencing revived interest and uptake in the urban environment. When implemented on a wide scale, RWH can contribute to both stormwater abatement and water conservation, serving to relieve pressure on existing infrastructure and potentially delay the need for infrastructure expansion. While such benefits are known, there remain several barriers that impede widespread implementation. These include cost, liability concerns, and a lack of clear policy for RWH. This paper outlines the benefits of RWH and describes findings of recent research that has attempted to develop some of the technical, administrative, and market capacity needed to overcome these barriers, focussing on water quality, design practices, economic analysis, and policy development.
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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 | 0.008 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".