Portuguese Football Federation consensus statement 2020: nutrition and performance in football
Bibliographic record
Abstract
Nutrition is an undeniable part of promoting health and performance among football (soccer) players. Nevertheless, nutritional strategies adopted in elite football can vary significantly depending on culture, habit and practical constraints and might not always be supported by scientific evidence. Therefore, a group of 28 Portuguese experts on sports nutrition, sports science and sports medicine sought to discuss current practices in the elite football landscape and review the existing evidence on nutritional strategies to be applied when supporting football players. Starting from understanding football's physical and physiological demands, five different moments were identified: preparing to play, match-day, recovery after matches, between matches and during injury or rehabilitation periods. When applicable, specificities of nutritional support to young athletes and female players were also addressed. The result is a set of practical recommendations that gathered consensus among involved experts, highlighting carbohydrates periodisation, hydration and conscious use of dietary supplements.
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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 | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".