Forecasting thermal stress for sports tourists at the 2026 FIFA World Cup
Bibliographic record
Abstract
This study explores thermal stress conditions (i.e. heat, humidity, sunlight exposure) sports tourists can expect when attending the 2026 FIFA World Cup. Sports tourism's growth is driven by international mega-events like FIFA World Cups and Olympic Games. With planning ongoing for the 2026 FIFA World Cup football (soccer) tournament, what remains unstudied are the heat conditions spectators can expect at the 16 host stadiums in the United States, Mexico, and Canada. The inquiry is important considering (1) the tournament is taking place in warm-weather months of June and July, (2) thermal stress contributes to heat-related illnesses like heat stroke, and (3) many destination-bound tourists will be at elevated risk to heat. Accordingly, we report historical and forecasted thermal stress levels documenting expected conditions at each host stadium. Notably, forecasts indicate worsening thermal stress compared to long-term means, providing support for the study's methodology. Practical implications and limitations are provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".