Groundwater Irrigation and Arsenic Speciation in Rice in Cambodia
Bibliographic record
Abstract
BACKGROUND: Arsenic bioaccumulation in rice is a global concern affecting food security and public health. OBJECTIVE: The present study examined arsenic species in rice in Cambodia to characterize health risks with rice consumption and to clarify uncertainties with Codex guidelines. METHODS: The present study collected 61 well water samples, 105 rice samples, 70 soil samples, and conducted interviews with 44 families in Preak Russey near the Bassac River and Kandal Province along the Mekong River in Cambodia. Analyses of metals, total arsenic and arsenic species were conducted in laboratories in Canada, Cambodia and Singapore. RESULTS: Unlike in Bangladesh, rice with the highest total arsenic concentrations in Cambodia contains mostly organic arsenic, dimethylarsinic acid (DMA), which is unregulated and much less toxic than inorganic arsenic. The present study found that storing surface runoff in ditches prior to irrigation can significantly reduce the arsenic concentration in rice. It is possible to remove > 95% of arsenic from groundwater prior to irrigation with natural reactions. CONCLUSIONS: The provision of high quality drinking water in 2015 to Preak Russey removed about 95% of the dietary inorganic arsenic exposure. The extremes in arsenic toxicity that are still obvious in these farmers should become less common. Rice from the site with the highest documented levels of arsenic in soils and water in Cambodia passes current Codex guidelines for arsenic. INFORMED CONSENT: Obtained. COMPETING INTERESTS: The authors declare no competing financial interests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".