An Experimental Study of Greywater Irrigated Green Roof Systems in an Arid Climate
Bibliographic record
Abstract
Green roofs provide multi-functional benefits to the built environment.They minimize urban heat island effects, enhance biodiversity, reduce carbon footprints, provide hydraulic benefits to urban runoff, and improve overall environmental sustainability.However, their application is limited or rare in arid climates.On the other hand, greywater is becoming a popular alternative water resource in water-scarce regions.A greywater-fed green roof system was developed and studied in the city of Al Ain, United Arab Emirates (UAE).The effluent (treated greywater) from the green roofs can be used to irrigate amenity plantations.Two intensive and two extensive green roof prototypes were constructed, planted with reed canary grass (Phalaris arundinacea L.), and irrigated with greywater.The greywater influents and the green roof effluents were monitored for changes in greywater quality.The study showed that the intensive system performed well, which is attributable to the greater depth of soil media.Treated greywater effluent from the green roofs met the local standards for recycled wastewater-based irrigation for a number of parameters (pH, electrical conductivity, salinity, and total dissolved solids), but exceeded the maximum allowable limits for turbidity, COD, and sodium ions (Na + ), which may be because of the short retention time of the experiment.Both the intensive and the extensive systems were inefficient in reducing the total bacterial count of the greywater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".