SPRINTING. . . Dietary Approaches to Optimize Training Adaptation and Performance
Bibliographic record
Abstract
Although sprint athletes are assumed to primarily be interested in promoting muscle hypertrophy, it is the ability to generate explosive muscle power, optimization of power-to-weight ratio, and enhancement of anaerobic energy generation that are key outcomes of sprint training. This reflects the physique of track sprinters, being characterized as ecto-mesomorphs. Although there is little contemporary data on sprinters dietary habits, given their moderate energy requirements relative to body mass, a carbohydrate intake within the range of 3–6 g·kg −1 ·day −1 appears reasonable, while ensuring carbohydrate availability is optimized around training. Similarly, although protein needs may be twice general population recommendations, sprint athletes should consume meals containing ∼0.4 g/kg high biological value protein (i.e., easily digested, rich in essential amino acids) every 3–5 hr. Despite the short duration of competitions and relative long-recovery periods between races, nutrition still plays an important role in sprint performance. As energy expenditure moderates during competition, so too should intake of energy and macronutrients to prevent unwanted weight gain. Further adjustments in macronutrient intake may be warranted among athletes contemplating optimization of power-to-weight ratio through reductions in body fat prior to the competitive season. Other novel acute methods of weight loss have also been proposed to enhance power-to-weight ratio, but their implementation should only be considered under professional guidance. Given the metabolic demands of sprinting, a few supplements may be of benefit to athletes in training and/or competition. Their use in competition should be preceded with trialing in training to confirm tolerance and perceived ergogenic potential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".