Alleviating health risks associated with rainwater harvesting
Bibliographic record
Abstract
Perceived and real public health risks associated with the quality of water from alternative water sources and supply systems, such as rainwater harvesting (RWH) and grey water reuse, continue to restrict their uptake in many countries. One option to alleviate these health risks is to treat alternative water to potable standard at the point of use (POU) as opposed to the point of supply, as undertaken in centralised systems. This paper presents the results of three international empirical field trials of a novel POU RWH treatment device. The results indicate that where the harvested rainwater did not contain elevated levels of pesticides or physico-chemical determinands, the POU device was able to reduce levels in outlet water to meet UK, EU and World Health Organization potable standards. Regarding microbiological determinands, such as total viable counts and coliforms, and microbial pathogens, such as Pseudomonas aeruginosa and Legionella spp., the device achieved reduction to potable standard and full pathogen removal, respectively. Thus, while it is possible to treat harvested rainwater to potable standard with a POU device, whether it is desirable to do so to alleviate risks for all end uses remains a question for further debate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".