Release of Iron, Zinc, and Lead from Common Iron Construction Bars and Zinc Metallic Bars in Water Solutions and Meals
Bibliographic record
Abstract
BACKGROUND: The use of iron pots has decreased the prevalence of anemia. OBJECTIVE: To investigate the release of iron, zinc, and lead from metallic iron and zinc bars incubated in water and in meals. METHODS: Iron, zinc, and lead concentrations were measured at different incubation conditions in water and in meals. RESULTS: The iron concentration in water was 1.26 mg/L after incubation with one iron bar at pH 7 and 100 degrees C for 20 minutes and in meals was 0.97 mg per 100 g of wet meals, rich in phytate, cooking at 100 degrees C during 20 minutes. The maximum contents were 7720 mg/L of iron and 1826 mg/L of zinc in vinegar at pH 3 and 20 degrees C after 90 and 32 days, respectively. Lead was released from the bars, but at concentrations well below the upper tolerable limits. DISCUSSION: In outreach populations, the use of iron and zinc metallic bars in water and meals could contribute to sustainable, very low-cost prevention of iron and zinc deficiencies, and home-fortified vinegar could be used for treatment of both deficiencies. CONCLUSIONS: Field trials should be performed to determine the impact that the use of iron and zinc metallic bars in water and meals might have on the iron and zinc status of population groups.
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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 | 0.000 |
| 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) | 0.001 | 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".