Human Health Risk Assessment: Arsenic Exposure Risks in Bangladesh
Bibliographic record
Abstract
Arsenic-caused cancers in Bangladesh, arising from both water and food sources of arsenic (As) are characterized. The results indicate countrywide incremental As cancer cases as 1.27 million, a number which will increase by 1.2% per year unless a sustainable removal technology for As from groundwater is implemented. The site-specific magnitude of the incremental cancer, driven by local groundwater conditions is demonstrated where, for example, in Chandpur, 4% of the districts’ population will develop cancer due to As intake. On average, 46% of the As body burden for Bangladeshis comes directly from water and 54% from food, although the range of percentages varies significantly from one district to the next, from a low of 0% water-based intake (indicating entire body burden from food sources) in the district of Dhaka, to a high of 91% in Chandpur. It is noteworthy that residents of Dhaka, since they are only exposed to food-related As, will see an estimated 42,000 incremental cancer cases. Treating drinking water to the Bangladeshi standard of 50µg/L would decrease incremental cancer cases in Bangladesh by 353,000; further reduction to the WHO standard of 10µg/L would reduce the number of incremental cancer cases by an additional 298,000.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".