Athlete Insights on Climate Change and Winter Sport: Impacts, Thresholds, Adaptations, and Implications for the Future
Bibliographic record
Abstract
Relying directly on snow, ice, and cold temperatures, outdoor winter sports are already experiencing and responding to climate change. Through an online survey of elite-level athletes and coaches (n = 390), and semi-structured key stakeholder interviews (n = 8), this research investigates the climate thresholds and adaptations that enable world-class performance in safe and fair competitions. Ideal competition conditions include temperatures within −1 to −10 °C, on consistent snow surfaces. Over 95% of respondents stated climate change is or will negatively impact their sport, with current adaptations ranging from good (snowmaking) to poor (canceled training runs). Beyond competitions, athletes and coaches are concerned climate change will reduce training opportunities, negatively impacting next-generation athlete development and winter sport culture. The results yield important insight into athlete and coach perspectives on climate change impacts, thresholds, and adaptations that can inform future policy, planning, and management for winter sport organizations at all levels.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".