Bibliographic record
Abstract
Rainwater samples were collected from rainwater harvesting (RWH) systems at seven sites located in a 30 km radius around the City of Guelph in Ontario, Canada. From October 2006 to October 2007, a total of 360 samples were collected from two sampling locations—the rainwater cistern and at the point of use—and analysed for pH, turbidity, colour, total and fecal coliforms, total organic carbon, total nitrogen and UV absorbance (254 nm). Additional parameters, including polycyclic aromatic hydrocarbons, total metals, Campylobacter and Legionella were examined in selected samples. Following data collection, statistical analysis was performed to investigate the factors that influenced rainwater quality. The results of the quality assessment programme were largely consistent with those reported by several other researchers, with the exception of improved microbiological quality during periods of cold weather. Total and fecal coliforms were detected in 31 % and 13 % of the rainwater samples, respectively, while neither Campylobacter nor Legionella were detected above 1 CFU/100 ml detection limits. The results indicate that, while quality can be expected to vary with environmental conditions, the rainwater from a RWH system can be of consistently high quality through the selection of appropriate catchment and storage materials and the application of post-cistern treatment. Key words | cistern, point-of-use, rainwater harvesting, water quality
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.282 | 0.108 |
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".