Metabolically Optimal Gait Transitions in Cross-Country Skate Skiing
Bibliographic record
Abstract
With increasing speeds of locomotion, animals change their gait pattern to minimize metabolic cost. For ex- ample, a horse will walk at low speeds, trot at intermediate speeds and gallop at high speeds [1]. Likewise, cross-country skiers use the 2-skate technique at slow speeds, switch to the 1-skate technique at intermediate speeds, but then, in contrast to everything known about locomotion in humans and animals, they revert back to the previously rejected 2-skate technique at very high speeds of locomotion. This pattern of gait transitions suggests that the metabolic efficiency curves for 1-skate and 2-skate skiing intersect twice, rather than just once as they do for human walking and running. The purpose of this study was to test if the metabolic efficiency curves of 1-skate and 2-skate skiing intersect twice, and if so, find an explanation for this surprising result. Eight nationally competitive skiers were tested on a roller ski treadmill. Subjects were asked to ski at speeds of 6-33km/h at 3km/h increments, once with the 1-skate technique and once with the 2-skate technique. Oxygen consumption and 3d kinematics were recorded continuously. We found that the metabolic efficiency curves intersected twice. We also found that pole contact time and distance was much shorter per stride when using the 1-skate technique, especially at very low and very high speeds.
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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 | 0.001 | 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) | 0.001 | 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".