UCI Sports Nutrition Project: Special Environments
Bibliographic record
Abstract
Elite cyclists frequently train and compete in extreme environments, such as heat, and cold conditions that are becoming more common due to climate change. Altitude training, aimed at preparing athletes for high-altitude events, is widely employed to enhance subsequent sea-level performance. Similarly, heat training has proven to have a similar transfer effect, in addition to the essential acclimatization effects, relevant for performance in hot conditions. Exposure to these challenging environments increases physiological stress raising energy and carbohydrate oxidation rates, and affecting overall performance. This Union Cycliste Internationale (UCI) consensus on nutrition for cycling addresses the nutritional challenges associated with extreme environmental conditions and explores tailored nutrition and hydration strategies to mitigate their effects. The review examines how heat, cold, and altitude affect hydration, energy expenditure, and metabolism, with associated macro- and micronutrient considerations, in cyclists. It discusses practical strategies for managing fluid balance, carbohydrate intake, and micronutrient and electrolyte supplementation and the use of ergogenic aids in supporting adaptation to environmental stresses. This review provides evidence-based nutrition and fluid recommendations for optimizing cycling performance and fostering adaptation in extreme environments. It offers practical guidance on nutrition and hydration strategies before, during, and after training and competition, helping cyclists maintain peak performance while navigating the unique challenges posed by these conditions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".