Current knowledge on playing football in hot environments
Bibliographic record
Abstract
Facts -The capacity to exercise is markedly reduced in hot environments. -High humidity imposes further physiological strain on the body. -The combination of exercise and hot environments increases the risk of heat illness and heatstroke. Recommendations for players -Fluid intake should be sufficient to restrict body mass loss to o2% of the pre-exercise mass. -A minimum of 1-2 weeks of acclimatization should be undertaken before playing in a hot environment. Recommendations for organizers -Perform pre-participation screenings before competing in the heat. -Watch at-risk individual (e.g. sickle cell trait, vascular disease) and observe signs and symptoms if WBGT > 28°C. -Add additional fluid breaks and foresee medical coverage if WBGT > 30°C. -Reconsider event if WBGT > 32°C. Future research should investigate -The optimal cooling strategies prior and during sporting activities, i.e., halftime in football games. -The influence of acclimatization on football performance. -The impact of heat on decision making in players and match officials. -The impact of heat on spectators from cooler regions.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".