Time to revisit arsenic regulations: comparing drinking water and rice
Bibliographic record
Abstract
BACKGROUND: Current arsenic regulations focus on drinking water without due consideration for dietary uptake and thus seem incoherent with respect to the risks arising from rice consumption. Existing arsenic guidelines are a cost-benefit compromise and, as such, they should be periodically re-evaluated. DISCUSSION: Literature data was used to compare arsenic exposure from rice consumption relative to exposure arising from drinking water. Standard risk assessment paradigms show that arsenic regulations for drinking water should target a maximum concentration of nearly zero to prevent excessive lung and bladder cancer risks (among others). A feasibility threshold of 3 μg As l(-1) was determined, but a cost-benefit analysis concluded that it would be too expensive to target a threshold below 10 μg As l(-1). Data from the literature was used to compare exposure to arsenic from rice and rice product consumption relative to drinking water consumption. The exposure to arsenic from rice consumption can easily be equivalent to or greater than drinking water exposure that already exceeds standard risks and is based on feasibility and cost-benefit compromises. It must also be emphasized that many may disagree with the implications for their own health given the abnormally high cancer odds expected at the cost-benefit arsenic threshold. SUMMARY: Tighter drinking water quality criteria should be implemented to properly protect people from excessive cancer risks. Food safety regulations must be put in place to prevent higher concentrations of arsenic in various drinks than those allowed in drinking water. Arsenic concentrations in rice should be regulated so as to roughly equate the risks and exposure levels observed from 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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".