Nutrition and Training Adaptations in Aquatic Sports
Bibliographic record
Abstract
The adaptive response to training is determined by the combination of the intensity, volume, and frequency of the training. Various periodized approaches to training are used by aquatic sports athletes to achieve performance peaks. Nutritional support to optimize training adaptations should take periodization into consideration; that is, nutrition should also be periodized to optimally support training and facilitate adaptations. Moreover, other aspects of training (e.g., overload training, tapering and detraining) should be considered when making nutrition recommendations for aquatic athletes. There is evidence, albeit not in aquatic sports, that restricting carbohydrate availability may enhance some training adaptations. More research needs to be performed, particularly in aquatic sports, to determine the optimal strategy for periodizing carbohydrate intake to optimize adaptations. Protein nutrition is an important consideration for optimal training adaptations. Factors other than the total amount of daily protein intake should be considered. For instance, the type of protein, timing and pattern of protein intake and the amount of protein ingested at any one time influence the metabolic response to protein ingestion. Body mass and composition are important for aquatic sport athletes in relation to power-to-mass and for aesthetic reasons. Protein may be particularly important for athletes desiring to maintain muscle while losing body mass. Nutritional supplements, such as b-alanine and sodium bicarbonate, may have particular usefulness for aquatic athletes' training adaptation.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".