Addition of Zinc to Soaking Water during Parboiling Increases the Zinc Content of Bangladeshi Rice
Bibliographic record
Abstract
Objectives: In Bangladesh, zinc deficiency affects 45% of preschool children and 57% of women. As zinc deficiency is linked to child growth stunting, diarrheal disease, pneumonia, and increased risk of child mortality, large-scale programs for its prevention are required. Most rice produced in Bangladesh is parboiled and this presents a technical opportunity to increase rice zinc content by adding zinc during soaking. The objective of this study was to evaluate the increase in zinc content achievable by this strategy in milled Bangladeshi rice using local parboiling conditions, and its potential effect on adequacy of zinc intakes. Methods: A major local rice variety (BR29) and zinc sulfate were used. Paddy was steamed for 2 minutes, soaked in distilled, deionized water for 9 hours with addition of 0, 100, 150, 200, or 300 mg zinc/kg paddy. Drained paddy was steamed in a pressurized autoclave before drying and milling. Zinc content was determined by X-Ray Fluorescence. Results: Rice zinc content was 12.3, 16.0, 16.7, 21.6, and 23.6 mg/kg dry weight, respectively, where the highest level represents a 92% increase over the control. Using existing dietary intake Conference
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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) | 0.002 | 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".