The Training of Olympic Alpine Ski Racers
Bibliographic record
Abstract
Alpine combined was the only alpine ski racing event at the first Winter Olympic Games in 1936, but since then, slalom, giant slalom, super-G, downhill, and team events have also become Olympic events. Substantial improvements in slope preparation, design of courses, equipment, and the skills of Olympic alpine skiers have all helped this sport attain its present significance. Improved snow preparation has resulted in harder surfaces and improved equipment allows a more direct interaction between the skier and snow. At the same time, courses have become more challenging, with technical disciplines requiring more pronounced patterns of loading - unloading, with greater ground reaction forces. Athletes have adapted their training to meet these new demands, but little is presently known about these adaptations. Here, we describe how Olympic athletes from four of the major alpine ski racing nations prepared for the Olympic Games in South Korea in 2018. This overview describes their typical exercise programs with respect to physical conditioning, ski training and periodization, based on interviews with the coaching staff. Alpine ski racing requires mastery of a broad spectrum of physical, technical, mental, and social skills. We describe how athletes and teams deal with the multifactorial nature of the training required. Special emphasis is placed on sport-specific aspects, such as the combination of stimuli that interfere with training, training with chronic injury, training at altitude and in cold regions, the efficiency and effectiveness of ski training and testing, logistic challenges and their effects on fatigue, including the stress of frequent traveling. Our overall goal was to present as complete a picture of the training undertaken by Olympic alpine skiers as possible and on the basis of these findings propose how training for alpine ski racing might be improved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".