Increasing Health Risks During Outdoor Sports Due To Climate Change in Texas: Projections Versus Attitudes
Bibliographic record
Abstract
Extreme heat is a recognized threat to human health. This study examines projected future trends of multiple measures of extreme heat across Texas throughout the next century, and evaluates the expected climate changes alongside Texas athletic staff (coach and athletic trainer) attitudes toward heat and climate change. Numerical climate simulations from the recently published Community Earth System Model version 2 and the Climate Model Intercomparison Project were used to predict changes in summer temperatures, heat indices, and wet bulb temperatures across Texas and also within specific metropolitan areas. A survey examining attitudes toward the effects of climate change on athletic programs and student athlete health was also distributed to high-school and university athletic staff. Heat indices are projected to increase beyond what is considered healthy/safe limits for outdoor sports activity by the mid-to-late 21st century. Survey results reveal a general understanding and acceptance of climate change and a need for adjustments in accordance with more dangerous heat-related events. However, a portion of athletic staff still do not acknowledge the changing climate and its implications for student athlete health and their athletic programs. Enhancing climate change and health communication across the state may initiate important changes to athletic programs (e.g., timing, duration, intensity, and location of practices), which should be made in accordance with increasingly dangerous temperatures and weather conditions. This work employs a novel interdisciplinary approach to evaluate future heat projections alongside attitudes from athletic communities toward climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".