Protective guidelines and mitigation strategies for hot conditions in professional football: starting 11 Hot Tips for consideration
Bibliographic record
Abstract
Elevated heat, humidity and solar load combined with low air movement independently and additively impair performance, increase the perception of effort and the risk of heat-related illnesses. For the specific context of professional football, the Fédération Internationale de Football Association (FIFA) heat guidelines are often used as the default policy. Still, these seem less protective than guidelines in other sports or from countries traditionally exposed to extreme hot conditions. Following several high-profile international and continental competitions played in hot conditions (eg, 2014 FIFA World Cup Brazil), a series of cross-sectional studies showed that national team players and their managers unanimously mentioned that the hot and humid conditions during these matches made it difficult as a team to perform. Such a concern is likely to be relevant for the upcoming 2026 FIFA World Cup that will be held in the traditional June-July window across 16 host cities in Canada, Mexico and USA. Therefore, to better protect players' health and performance during training and match play in hot conditions, we present our starting 11 Hot Tips that should be considered and facilitated by governing bodies, competition organisers, clubs, staff members and players.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".