Food Fortification with Vitamin A: The Potential for Contributing to the Elimination of Vitamin A Deficiency in Africa
Bibliographic record
Abstract
The control of vitamin A deficiency is a realizable goal that needs to be addressed through a combination of interventions. Among these, fortification of commonly eaten foods and condiments has great potential to help realize this goal. Fortification offers a number of strategic advantages, because it is cost effective, builds on existing food processing and delivery systems, and enhances sustainability. Proven vehicles for vitamin A fortification that are relevant for Africa include sugar, oils and fats, and cereal flours. Fortified foods cannot be expected to reach all deficient populations. However, for the large and expanding populations of all socioeconomic classes that regularly purchase and consume commercially processed foods, fortification can make an enormous difference. For those who do not have easy access to commercially processed foods, fortification at community-level mills and the use of encapsulated micronutrient sprinkles are promising technologies. Although most technologies described in this paper are ready for scale-up and large-scale application, there are a number of steps to be undertaken in order to ensure effective programs, including appropriate advocacy and communication, collaboration among several sectors, food regulations and standards, and quality assurance and monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".