A tribological perspective on friction and performance in Olympic snow and ice sports
Bibliographic record
Abstract
Abstract With 62% of medals at the upcoming 2026 Winter Olympics in Milano-Cortina to be awarded in skiing disciplines and the remaining 38% in ice-based events, understanding the determinants of performance is critical. Despite extensive examination of athletes’ physiological, biomechanical, and psychological attributes, the role of tribology—particularly in understanding friction on snow and ice—has received less attention. This is a scientific perspective article outlining key tribological factors and highlighting their importance in Olympic winter sports. In skiing, optimising the ski–snow interaction requires a comprehensive understanding of how friction is influenced by snow crystal morphology, temperature, ski base material and structure, ski stiffness, skier technique, and environmental conditions. For ice-based events, friction is determined by a combination of ice surface roughness, temperature, and sport-specific preparation techniques, as well as equipment design (e.g. blade material and geometry, the running surface finish of curling stones) and athlete technique (e.g. angle of attack in speed skating, sweeping in curling). Ice preparation techniques further influence friction, with specific conditions tailored to each sport. In conclusion, advancements in Olympic winter sports have been significant. However, future breakthroughs in performance may lie in applying tribological insights to optimise the complex interactions between athletes, equipment, and the unique properties of snow and ice. This perspective article aims to guide future research by synthesising current understanding and identifying emerging challenges in winter sports tribology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".