Iron content of Cambodian foods when prepared in cooking pots containing an iron ingot
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of cooking with an iron ingot on the iron content of several water and Cambodian food preparations. METHODS: Various food and water samples were prepared, in replicate, in glass and aluminium pots with and without an iron ingot. The samples were subjected to iron content analysis using standard ICP-OES procedures. RESULTS: Prepared with an ingot, the iron content was 76.3 μg iron/g higher in lemon water, 32.6 μg iron/g higher in pork soup and 3.3 μg iron/g higher in fish soup, than in the same foods prepared without an ingot. Acidity of the food samples was positively associated with iron leaching. CONCLUSIONS: Even when taking into account the low bioavailability of contaminant iron, approximately 75% of the daily iron requirement can be met by consuming 1L of lemon water prepared with an iron ingot. Its use may be a cheap and sustainable means of improving iron intake for those with iron-deficient diets.
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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".