Interactions of Dimethylarsinic Acid, Total Arsenic and Zinc Affecting Rice Crop Management and Human Health in Cambodia
Bibliographic record
Abstract
BACKGROUND: In parts of Cambodia and in many other parts of the world, irrigation of rice with groundwater results in arsenic (As) accumulation in soil and rice, leading to health concerns associated with rice consumption. At times, some As is present as relatively nontoxic, non-regulated, dimethylarsinic acid (DMA). Low levels of zinc (Zn) have been found in rice from Bangladesh, Cambodia, and China where As levels in rice are high. Furthermore, there have been claims that Zn deficiency is responsible for stunting the growth of children in Cambodia and elsewhere, however in rural Asia, rice is the major source of Zn. Current data are inadequate for both Zn and DMA in Cambodian rice. OBJECTIVES: The present study aimed to provide a preliminary evaluation of the relationship between the content of Zn and DMA in rice grain in Preak Russey, an area with elevated levels of As in groundwater and to improve the management of Zn deficiency in rice. METHODS: Rice agriculture was evaluated along the Mekong River in Cambodia. Analyses for metals, total As, and As species in rice and water were conducted by inductively coupled plasma mass spectrometry. Analysis of total Zn and As in soils and total Zn in rice were analyzed using X-ray fluorescence (XRF) spectrometry. RESULTS: Rice in Preak Russey had Zn concentrations less than a third the level recommended by the United Nations World Food Programme. There was a significant (p < 0.05) negative correlation between the Zn content of rice and DMA in rice with the lowest Zn and highest DMA levels occurring near irrigation wells, the source of As. CONCLUSIONS: The highest levels of DMA in rice were associated with Zn deficiency in rice. 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".