Efficacy of an appropriate point-of-use water treatment intervention for low-income communities in India utilizing Moringa oleifera, sari-cloth filtration and solar UV disinfection
Bibliographic record
Abstract
This study investigated the efficacy of a POU water treatment system featuring sari-cloth filtration and/or Moringa oleifera coagulation as pre-treatments for solar disinfection (SODIS). Surface water from a peri-urban slum in Chennai, India, was treated and analysed for turbidity, organic content via chemical oxygen demand (COD) and microbiological quality via most probable number (MPN) enumeration of total coliforms. Pre-treatment with both moringa coagulation and sari-cloth filtration significantly improved the turbidity of raw water compared to no pre-treatment controls (P = 0.0002). Optimal moringa coagulation did not outperform sari-cloth filtration (P = 0.06), but combining optimal moringa coagulation with sari-cloth filtration significantly outperformed either pre-treatment independently with respect to turbidity (P = 0.016 and P = 0.0001, respectively). The addition of moringa was found to increase COD in treated water, with greater doses of moringa resulting in higher COD levels (P = 0.04). Increased organics may have encouraged the re-growth of coliform bacteria that was observed in those jars receiving moringa coagulant such that, with respect to MPN, those jars which were subject to optimal moringa coagulation did not outperform those undergoing sari-cloth filtration alone (P = 0.41). Sari-cloth filtration is recommended as a pre-treatment for SODIS whereas moringa is not, as further investigation on the relationship between organics and bacterial re-growth is necessary.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".