On Ski–Snow Contact Mechanics During the Double Poling Cycle in Cross-Country Skiing
Bibliographic record
Abstract
Abstract Of the medals awarded during the Winter Olympics Games, most are awarded for sports involving cross-country (XC) skiing. The Double Poling (DP) technique, which is one of the sub-techniques used most frequently in XC skiing, has not yet been studied using simulations of the ski–snow contact mechanics. This work introduces a novel method for analysing how changes in the distribution of pressure on the sole of the foot (Plantar Pressure Distribution or PPD) during the DP motion affect the contact between the ski and the snow. The PPD recorded as the athlete performed DP, along with an Artificial Neural Network trained to predict the geometry of the ski (ski-camber profile), were used as input data for a solver based on the boundary element method, which models the interaction between the ski and the snow. This solver provides insights into how the area of contact and the distribution of pressure on the ski-snow interface change over time. The results reveal that variations in PPD, the type of ski, and the stiffness of the snow all have a significant impact on the contact between the ski and the snow. This information can be used to improve the Double Poling technique and make better choices of skis for specific snow conditions, ultimately leading to improved performance. Graphical Abstract
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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 | 0.000 | 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) | 0.003 | 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".