Retrofitting arsenic-iron removal plants in rural Bangladesh for performance enhancement
Bibliographic record
Abstract
As a result of naturally occurring arsenic in groundwater, it is estimated that 42–60 million people in Bangladesh are exposed to arsenic at concentrations greater than the World Health Organization (WHO) guideline of 10 μg/L. Arsenic-Iron Removal Plants (AIRPs) are capable of removing 50–90% of arsenic from groundwater, but are frequently unable to meet the WHO guideline. The effectiveness of three design modifications intended to improve the performance of AIRPs is described: (1) the addition of scrap or locally available iron to the filtration media, (2) raising the intake pipe that connects the two tanks of the AIRP, and (3) introducing baffles to the aeration tank. Total arsenic, iron, phosphate, and dissolved oxygen were measured to determine the impact of each modification. The addition of iron media showed an increase in arsenic removal up to 13%, while raising the pipe intake accounted for a 3% increase in arsenic removal. The installation of both modifications to the same AIRP is expected to reduce the lifetime body burden from drinking water by one-half. The addition of baffles to the aeration tank showed no evidence of improving the arsenic removal capabilities of the AIRP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".