Contribution of the Upper-Body in Skate Cross-Country Skiing
Bibliographic record
Abstract
The skate technique in cross-country skiing has a unique gait transition. Typically, skiers will use the two-skate technique at low speeds, transition to the one-skate technique at intermediate speeds, then return to the two-skate technique at high speeds. We hypothesize that this unique gait transition can be explained by differences in the contribution of the arms to propulsion and the associated metabolic cost of upper-body and arm work. In one-skate, poles are planted simultaneously with every skate stride, while in two-skate, poles are planted with every second skate stride (Smith, 2000). Using four trained cross-country ski racers, two separate tests were performed for each technique of skate skiing. First, subjects skied at 6, 15, and 30 km/h on a rollerski treadmill. During the entire test VO2, pole force, lactate, and video were recorded for one technique and repeated on another day using the second technique. In the second phase of testing, the poling motion only was simulated on a pole ergometer with subjects matching their stroke rate and poling forces using video and force feedback. Upper-body VO2 and lactate were measured and compared to the treadmill test values. The average metabolic cost associated with the upper-body work was 60% of the total metabolic cost when skiing on the treadmill. The upper-body metabolic cost was always higher for the one skate compared to the two skate technique. At slow speeds the difference between the two techniques was small (3%), but this difference increased at higher speeds from 10% at 15km/h to 14% at 30km/h. The poling motion associated with one-skate becomes more metabolically costly than two-skate as speed increases. A skier’s regressive transition from one-skate to two-skate at high speeds may be explained by a need to transfer impulse generation to the legs, since the sliding limbs remain effective at high speeds while the fixed limbs (poles) become less effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".