Vitamin D Fortification and Its Effect on Athletes’ Physical Improvement: A Mini Review
Bibliographic record
Abstract
Poor vitamin D status is a widespread problem regardless of age and sex, emphasizing the necessity of new food sources to improve vitamin D levels. Currently, approximately 60% of dietary vitamin D consumption occurs via fortified foods. Vitamin D insufficiency (50-90%) is widespread according to age and region, despite different levels of sunlight exposure. The food industry must identify more effective strategies to increase normal dietary vitamin D intake and improve overall health. Strategies for vitamin D fortification include bioaddition, wherein a vitamin D-rich food source is added to staple foods during processes. These bioadditive strategies expand the range of vitamin D-containing foods and appeal to different preferences, cultures, and economic statuses. In several countries, vitamin D deficiency places athletes at a high risk of disease susceptibility. Due to low sun exposure, athletes in countries with higher and lower levels of sunlight have similar risks of vitamin D deficiency. In this review, we summarize recent technical advances to promote vitamin D utilization by humans during sports activities and in relation to the normal practices of athletes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".