A performance assessment of arsenic-iron removal plants in the Manikganj District of Bangladesh
Bibliographic record
Abstract
In Bangladesh, arsenic contamination of groundwater, microbial contamination of surface water and seasonally variable rainfall make reliable access to acceptable quality drinking water a challenge. Arsenic-iron removal plants (AIRPs) are a relatively inexpensive way of removing arsenic from groundwater for access to safer drinking water. This study evaluated the performance of 21 (of 105) AIRPs installed by a local non-governmental organization (Society for People's Action in Change and Equity) with financial assistance from the Australian High Commission, Dhaka, under the Direct Aid Program of the Australian Government. All AIRPs achieved the Bangladesh standard for arsenic in drinking water of 50 microg L(-1) and 17 achieved the World Health Organization guideline of 10 microg L(-1). The AIRPs removed 87% of influent arsenic, on average. After cleaning, poor arsenic and iron removal was observed for about 2 days due to inadequate residence time. Chemical processes that may influence AIRP performance are also discussed herein, including iron and arsenic oxidation, arsenic co-precipitation with iron, multiple iron additions, interference by organics, and iron crystallization. Effluent faecal coliform counts were generally low, though were slightly higher than influent counts. Overall, AIRPs were shown to possess considerable promise for use in areas with high natural iron where users are concerned about arsenic and/or iron in their drinking water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".