Elevated levels of iron in groundwater in Prey Veng province in Cambodia: a possible factor contributing to high iron stores in women
Bibliographic record
Abstract
Iron is a natural element found in food, water and soil and is essential for human health. Our aim was to determine the levels of iron and 25 other metals and trace elements in groundwater from 22 households in Prey Veng, Cambodia. Water analyses were conducted using inductively coupled plasma-mass spectrometry and optical emission spectrometry. Compared to the 2011 World Health Organization guidelines for drinking water quality, aluminum, iron and manganese exceeded maximum levels (in 4.5, 72.7 and 40.9% of samples, respectively). Compared to the 2004 Cambodian drinking water quality standards, iron and manganese exceeded maximum levels (in 59.1 and 36.4% of samples, respectively). We found no evidence of arsenic contamination. Guidelines for iron were established primarily for esthetic reasons (e.g. taste), whereas other metals and elements have adverse effects associated with toxicity. Iron in groundwater ranged from 134 to 5,200 μg/L (mean ∼1,422 μg/L). Based on a daily consumption of 3 L groundwater, this equates to ∼0.4-15.6 mg iron (mean ∼4.3 mg/day), which may be contributing to high iron stores and the low prevalence of iron deficiency anemia in Prey Veng women. Elevated levels of manganese in groundwater are a concern and warrant further investigation.
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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 | 0.001 | 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".