Impact of skier actions on the gliding times in alpine skiing
Bibliographic record
Abstract
Alpine ski races are typically won by fractions of a second. It is therefore essential for ski racers to minimize air drag as well as ski-snow friction. In contrast to air drag, ski-snow friction during actual skiing has rarely been investigated so far. Two tasks, forward/backward leaning and edging of the skis, were selected, which (a) were expected to have an impact on ski-snow friction, and (b) could be executed while gliding in tucked position. Two hypotheses were tested: (H1) Run times are affected by forward or backward leaning. (H2) Run times are affected by edging of the skis. Four professional ski testers were recruited, who conducted a total of 68 runs of straight gliding. Execution of the tasks was documented by video recordings and by measuring the force application point on the skis of one tester. The findings of this study support (H2) but not (H1). There are indications that the increased run times for edging are caused by increased ski-snow friction. From a performance point of view, it seems beneficial for ski racers to minimize edging in the gliding sections of a race.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".