Selective electrodialysis technology: A viable solution to water quality challenges in Sri Lankan community-based organizations
Bibliographic record
Abstract
Safe drinking water is essential for public health, especially in rural communities where access to reliable water sources is limited. Ensuring water quality is critical to preventing waterborne diseases and supporting sustainable development. Community-based organizations (CBOs) are vital in managing rural water needs, yet they face significant groundwater quality issues. This study analyzed water quality parameters, revealing variability in Water Quality Index (WQI) classifications, which ranged from excellent to unsuitable, necessitating targeted treatment solutions. Principal component analysis (PCA) identified three key groundwater issues: brackish water with high fluoride, turbid water, and saline water. Among the treatment options, selective electrodialysis (SED) proved most effective for fluoride-enriched brackish groundwater. Over a 12 month s period, SED consistently met Sri Lankan drinking water standards, overcoming challenges such as membrane fouling. This technology offers a sustainable solution for improving groundwater quality in CBOs, thereby enhancing public health and ensuring safe drinking water in rural Sri Lanka.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".