Biomechanical analysis of the “running” vs. “conventional” diagonal stride uphill techniques as performed by elite cross-country skiers
Bibliographic record
Abstract
This study aimed to compare biomechanical aspects of a novel “running” diagonal stride (DSRUN) with “conventional” diagonal stride (DSCONV) skiing techniques performed at high speed. Ten elite Italian male junior cross-country skiers skied on a treadmill at 10 km/h and at a 10° incline utilizing both variants of the diagonal stride technique. The 3-dimensional kinematics of the body, poles, and roller skis; the force exerted through the poles and foot plantar surfaces; and the angular motion of the leg joints were determined. Compared to DSCONV, DSRUN demonstrated shorter cycle times (1.05 ± 0.05 s vs. 0.75 ± 0.03 s (mean ± SD), p < 0.001) due to a shorter rolling phase (0.40 ± 0.04 s vs. 0.09 ± 0.04 s, p < 0.001); greater force applied perpendicularly to the roller skis when they had stopped rolling forward (413 ± 190 N vs. 890 ± 170 N, p < 0.001), with peak force being attained earlier; prolonged knee extension, with a greater range of motion during the roller ski-stop phase (28° ± 4° vs. 16° ± 3°, p = 0.00014); and more pronounced hip and knee flexion during most of the forward leg swing. The mechanical work performed against friction during rolling was significantly less with DSRUN than with DSCONV (0.04 ± 0.01 J/m/kg vs. 0.10 ± 0.02 J/m/kg, p < 0.001). Our findings demonstrate that DSRUN is characterize by more rapid propulsion, earlier leg extension, and a greater range of motion of knee joint extension than DSCONV. Further investigations, preferably on snow, should reveal whether DSRUN results in higher acceleration and/or higher peak speed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".