Fortification and Health: Challenges and Opportunities
Bibliographic record
Abstract
Fortification is the process of adding nutrients or non-nutrient bioactive components to edible products (e.g., food, food constituents, or supplements). Fortification can be used to correct or prevent widespread nutrient intake shortfalls and associated deficiencies, to balance the total nutrient profile of a diet, to restore nutrients lost in processing, or to appeal to consumers looking to supplement their diet. Food fortification could be considered as a public health strategy to enhance nutrient intakes of a population. Over the past century, fortification has been effective at reducing the risk of nutrient deficiency diseases such as beriberi, goiter, pellagra, and rickets. However, the world today is very different from when fortification emerged in the 1920s. Although early fortification programs were designed to eliminate deficiency diseases, current fortification programs are based on low dietary intakes rather than a diagnosable condition. Moving forward, we must be diligent in our approach to achieving effective and responsible fortification practices and policies, including responsible marketing of fortified products. Fortification must be applied prudently, its effects monitored diligently, and the public informed effectively about its benefits through consumer education efforts. Clear lines of authority for establishing fortification guidelines should be developed and should take into account changing population demographics, changes in the food supply, and advances in technology. This article is a summary of a symposium presented at the ASN Scientific Sessions and Annual Meeting at Experimental Biology 2014 on current issues involving fortification focusing primarily on the United States and Canada and recommendations for the development of responsible fortification practices to ensure their safety and effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".