Design for sustainable development—Household drinking water filter for arsenic and pathogen treatment in Nepal
Bibliographic record
Abstract
In the last 20 years, the widespread adoption of shallow tubewells in Nepal Terai region enabled substantial improvement in access to water, but recent national water quality testing showed that 3% of these sources contain arsenic above the Nepali interim guideline of 50 microg/L, and up to 60% contain unsafe microbial contamination. To combat this crisis, MIT, ENPHO and CAWST together researched, developed and implemented a household water treatment technology by applying an iterative, learning development framework. A pilot study comparing 3 technologies against technical, social, and economic criteria showed that the Kanchan Arsenic Filter (KAF) is the most promising technology for Nepal. A two-year technical and social evaluation of over 1000 KAFs deployed in rural villages of Nepal determined that the KAF typically removes 85-90% arsenic, 90-95% iron, 80-95% turbidity, and 85-99% total coliforms. Then 83% of the households continued to use the filter after 1 year, mainly motivated by the clean appearance, improved taste, and reduced odour of the filtered water, as compared to the original water source. Although over 5,000 filters have been implemented in Nepal by January 2007, further research rooted in sustainable development is necessary to understand the technology diffusion and scale-up process, in order to expand access to safe water in the country and beyond.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".