Nutrition for Special Populations: Young, Female, and Masters Athletes
Bibliographic record
Abstract
Adolescent, female, and masters athletes have unique nutritional requirements as a consequence of undertaking daily training and competition in addition to the specific demands of age- and gender-related physiological changes. Dietary education and recommendations for these special population athletes require a focus on eating for long-term health, with special consideration given to "at-risk" dietary patterns and nutrients (e.g., sustained restricted eating, low calcium, vitamin D and/or iron intakes relative to requirements). Recent research highlighting strategies to address age-related changes in protein metabolism and the development of tools to assist in the management of Relative Energy Deficiency in Sport are of particular relevance to special population athletes. Whenever possible, special population athletes should be encouraged to meet their nutrient needs by the consumption of whole foods rather than supplements. The recommendation of dietary supplements (particularly to young athletes) overemphasizes their ability to manipulate performance in comparison with other training/dietary strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".