Design, Construction, and Operation of a Demonstration Rainwater Harvesting System for Greenhouse Irrigation at McGill University, Canada
Bibliographic record
Abstract
Increasing stress on urban water demand has led to the exploration of the potential of rainwater use and water recycling to promote sustainable water resources management. Rainwater harvesting (RWH) not only has the potential to reduce water demand but also contributes to other sustainable objectives, including reducing stormwater pollutant loads, reducing erosion, and inducing natural flow regimes by means of flood control, in urban streams. This research involved the design, construction, and field-testing of an RWH system used to irrigate greenhouses at the Macdonald Campus of McGill University in Quebec, Canada. The purpose of the RWH system was to collect rainwater from a roof area of ≈610 m 2 (the Horticulture Services Building on the Macdonald Campus of McGill University) to meet the irrigation demands of the two Horticulture Research Center greenhouses on the campus (≈149 m 2 each) from May to October. Over its two years of operation, it was found that the amount of rainwater collected did not only meet the peak irrigation demands of the greenhouses (which amounted to almost 700 gal of water per day), but that there was also enough water for the irrigation of the nearby student-run gardens. The harvested rainwater was clear and did not cause any harm to the plants. The major problem that was experienced during the operation of the RWH system was that of algae growth in one of the water collection tanks. This issue was resolved by covering the tank with metallic green wallpaper, thereby blocking most of the sunlight from entering the tank. The RWH system is currently being used for irrigation and as a demonstration project to promote the learning of sustainable technologies on campus and in the surrounding communities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".