International Association of Athletics Federations Consensus Statement 2019: Nutrition for Athletics
Bibliographic record
Abstract
The International Association of Athletics Federations recognizes the importance of nutritional practices in optimizing an Athlete's well-being and performance. Although Athletics encompasses a diverse range of track-and-field events with different performance determinants, there are common goals around nutritional support for adaptation to training, optimal performance for key events, and reducing the risk of injury and illness. Periodized guidelines can be provided for the appropriate type, amount, and timing of intake of food and fluids to promote optimal health and performance across different scenarios of training and competition. Some Athletes are at risk of relative energy deficiency in sport arising from a mismatch between energy intake and exercise energy expenditure. Competition nutrition strategies may involve pre-event, within-event, and between-event eating to address requirements for carbohydrate and fluid replacement. Although a "food first" policy should underpin an Athlete's nutrition plan, there may be occasions for the judicious use of medical supplements to address nutrient deficiencies or sports foods that help the athlete to meet nutritional goals when it is impractical to eat food. Evidence-based supplements include caffeine, bicarbonate, beta-alanine, nitrate, and creatine; however, their value is specific to the characteristics of the event. Special considerations are needed for travel, challenging environments (e.g., heat and altitude); special populations (e.g., females, young and masters athletes); and restricted dietary choice (e.g., vegetarian). Ideally, each Athlete should develop a personalized, periodized, and practical nutrition plan via collaboration with their coach and accredited sports nutrition experts, to optimize their performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".