How muscles maximize performance in accelerated sprinting
Bibliographic record
Abstract
We sought to provide a more comprehensive understanding of how the individual leg muscles act synergistically to generate a ground force impulse and maximize the change in forward momentum of the body during accelerated sprinting. We combined musculoskeletal modelling with gait data to simulate the majority of the acceleration phase (19 foot contacts) of a maximal sprint over ground. Individual muscle contributions to the ground force impulse were found by evaluating each muscle's contribution to the vertical and fore-aft components of the ground force (termed "supporter" and "accelerator/brake," respectively). The ankle plantarflexors played a major role in achieving maximal-effort accelerated sprinting. Soleus acted primarily as a supporter by generating a large fraction of the upward impulse at each step whereas gastrocnemius contributed appreciably to the propulsive and upward impulses and functioned as both accelerator and supporter. The primary role of the vasti was to deliver an upward impulse to the body (supporter), but these muscles also acted as a brake by retarding forward momentum. The hamstrings and gluteus medius functioned primarily as accelerators. Gluteus maximus was neither an accelerator nor supporter as it functioned mainly to decelerate the swinging leg in preparation for foot contact at the next step. Fundamental knowledge of lower-limb muscle function during maximum acceleration sprinting is of interest to coaches endeavoring to optimize sprint performance in elite athletes as well as sports medicine clinicians aiming to improve injury prevention and rehabilitation practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".