Quadriceps Muscle Function during Recreational Alpine Skiing
Bibliographic record
Abstract
PURPOSE: Since the introduction of carving skis, muscle activity has been investigated primarily on expert-level skiers with respect to EMG intensities. The three-part aim of this recreational skiing study was to analyze functional differences within the quadriceps muscle, to analyze the topographical influence, and to apply a time-frequency analysis of the EMG intensities using wavelets. METHODS: Seven female subjects performed two runs through a standardized corridor on a slope with different inclinations (13 degrees , 29 degrees , and 21 degrees ). Knee angle and EMG of vastus lateralis (VL) and rectus femoris (RF) of the right leg were measured during the runs. The recorded EMG signal was resolved with a set of 10 wavelets (11-432 Hz) into a time-frequency space. Subsequently, the EMG intensity and mean frequency (MF) were calculated for different time windows (inside leg; outside leg). RESULT: For RF, a significantly higher MF (+15.5%, P = 0.009) but similar EMG intensities were detected in the inside leg compared with the outside leg. For VL, the MF (-9.6%, P = 0.053) and EMG intensities (-54.3%, P = 0.010) were lower in the inside leg compared with the outside leg. Both muscles responded with higher EMG intensities on increasing slope inclination (VL = 90.8%, P = 0.022; RF = 115%, P = 0.01). MF is not directly related to inclination. CONCLUSIONS: Contrary to previously suggested coloading of the inside leg while carving, our results do not support this hypothesis for VL. However, the functional demand for RF in the inside leg is very high when skiing recreationally. The ability of a situation-dependent loading (RF as knee extensor) and unloading (RF as hip flexor) of the inside leg seems to be a crucial point with respect to specific fatigue during a skiing day.
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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 | 0.000 | 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".