Estimating nutrient fortification levels in condiments and seasonings for public health programs: considerations and adaptations
Bibliographic record
Abstract
Condiments and seasonings have been considered as potential vehicles for fortification in place of, or in addition to, fortifiable staple foods. Methodologies for establishing fortification programs focus primarily on use of staple foods, which are consumed in larger portions than condiments and seasonings. Some fortification models assume self-limiting consumption relative to the maximum energy consumed by target populations. However, this assumption may prove incorrect for estimating fortification concentrations of condiments and seasonings because they may only provide negligible energy. Although flavor or color may limit consumption, these limits would vary across each condiment or seasoning vehicle. In addition, the small volume of condiments and seasonings consumed relative to staple foods can lead to proportionally larger potential errors than with staple foods when measuring usual dietary intakes for establishing safe and effective fortification concentrations. This paper reviews available methods for setting fortification levels, whether or how available methods or conceptual frameworks could be adapted to condiments and seasonings, and gaps in knowledge for appropriately using condiments and seasonings as vehicles for fortification in public health.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".