On the influence of grip wax on ski–snow friction during the double poling cycle in cross-country skiing
Bibliographic record
Abstract
Abstract This study evaluates the negative effects of grip wax application on the dynamic ski–snow coefficient of friction and subsequent performance during the double poling cycle in cross-country skiing. Utilising a linear ski tribometer, friction tests were performed on classic cross-country skiing skis prepared with no, thin, and thick grip wax under controlled laboratory conditions. The dynamic coefficient of friction was estimated under various load conditions, reflecting dynamic skiing motions. Results indicated a clear increase in coefficient of friction with the addition of grip wax, with significant differences observed between thin and thick applications. Specifically, compared to skis with no wax, the coefficient of friction for skis with thin and thick wax layers experienced a negative increased by 1.8% and 3.2% during double poling, and by 1.7% and 2.6% whilst gliding, respectively. These friction increases were associated with higher power requirements during skiing or a consequent time loss. This underscores the need for meticulous application of grip wax application, tailored to the snow conditions, ski camber profile and racecourse demands, to minimise impact on gliding performance whilst maintaining sufficient static coefficient of friction for effective use of the diagonal stride technique. Furthermore, the skier should utilise a skiing technique to minimise the risk of encountering load conditions that increase the coefficient of friction. Overall, this research provides quantitative insights into the trade-offs between grip enhancement and friction-related performance losses in cross-country skiing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".