Blackcurrants: A Nutrient-Rich Source for the Development of Functional Foods for Improved Athletic Performance
Bibliographic record
Abstract
Blackcurrants are nutrient-rich fruits with a significant amount of bioactive compounds including vitamin C and polyphenols, especially anthocyanins. The high phytochemical content of blackcurrants promotes this fruit to become a valuable functional food ingredient with varying health-promoting activities targeting different consumers including athletes. Athletes experience oxidative stress during intense exercise, which can result in inflammation and reduced exercise performance. Antioxidants such as vitamin C and polyphenols can restore the regular oxidative status of the body. Blackcurrant supplementation has shown potential ergogenic activity to improve athlete performance during high-intensity training. Clinical trials have evaluated the effectiveness of blackcurrant supplementation on exercise performance, fat oxidation, blood lactate levels, muscle fatigue, and cardiac output. Due to the rich nutritional value of blackcurrants, they can be a potential candidate for the development of functional foods targeted at the improved performance of athletes. Blackcurrants can be used as ingredients to develop functional beverages and snacks for athletes as well as gluten-free products for celiac athletes.Blackcurrant is rich in bioactive compounds that can help improve athletic performance. It can be considered a potential bioactive ingredient to develop functional foods for athletes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".