Nutrition for distance events
Bibliographic record
Abstract
The goal of training is to prepare the distance athlete to perform at his or her best during major competitions. Whatever the event, nutrition plays a major role in the achievement of various factors that will see a runner or walker take the starting line in the best possible form. Everyday eating patterns must supply fuel and nutrients needed to optimize their performance during training sessions and to recover quickly afterwards. Carbohydrate and fluid intake before, during, and after a workout may help to reduce fatigue and enhance performance. Recovery eating should also consider issues for adaptation and the immune system that may involve intakes of protein and some micronutrients. Race preparation strategies should include preparation of adequate fuel stores, including carbohydrate loading for prolonged events such as the marathon or 50-km walk. Fluid and carbohydrate intake during races lasting an hour or more should also be considered. Sports foods and supplements of value to distance athletes include sports drinks and liquid meal supplements to allow nutrition goals to be achieved when normal foods are not practical. While caffeine is an ergogenic aid of possible value to distance athletes, most other supplements are of minimal benefit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".