Groundwater Irrigation and Arsenic Speciation in Rice in Cambodia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it