The impact of rainfall and seasonal variability on the removal of bacteria by a point-of-use drinking water treatment intervention in Chennai, India
Bibliographic record
Abstract
Point-of-use water treatment has received widespread application in the developing world to help mitigate waterborne infectious disease. This study examines the efficacy of a combined filter and chemical disinfection technology in removing bacterial contaminants, and more specifically changes in its performance resulting from seasonal weather variability. During a 12-month field trial in Chennai, India, mean log-reductions were 1.51 for E. coli and 1.67 for total coliforms, and the highest concentration of indicator bacteria in treated water samples were found during the monsoon season. Analysis of variance revealed significant differences in the microbial load of indicator organisms (coliforms and E. coli) between seasons, storage time since treatment (TST), and samples with and without chlorine residuals. Findings indicate that the bacteriological quality of drinking water treated in the home is determined by a complex interaction of environmental and sociological conditions. Moreover, while the effect of disinfection was independent of season, the impact of storage TST on water quality was found to be seasonally dependent.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".