Development of Equipment for Ski Mountaineering, a New Olympic Event
Bibliographic record
Abstract
Ski mountaineering, a new Olympic winter sport involving both climbing and descending snowy slopes, requires considerable physical and technical abilities, as well as highly specialized equipment. Herein, we briefly describe this equipment and its influence on performance and consider potential future advances. Skis, boots, and bindings must be light enough to facilitate climbing uphill (in which as much as 85% of the total racing time is spent) and, at the same time, provide stability and safety in often-challenging descents. A skier must be able to easily and rapidly attach and remove the adhesive skins under the skis that provide grip while skiing uphill. Poles and their baskets must be designed optimally to transfer propulsive force and help maintain balance. Despite the popularity of ski mountaineering, research on this sport is scarce, and we indicate a number of areas wherein improvements in equipment could potentially advance both performance and safety. Such advances must be based on a better understanding of the biomechanics of ski mountaineering, which could be obtained with novel sensor technology and can be best achieved via more extensive collaboration between researchers, skiers and their coaches, and manufacturers of ski mountaineering equipment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".