The influence of training-induced sarcomerogenesis on the history dependence of force
Bibliographic record
Abstract
Abstract The increase or decrease in isometric force following active muscle lengthening or shortening, relative to a reference isometric contraction at the same muscle length and level of activation, are referred to as residual force enhancement (rFE) and residual force depression (rFD), respectively. The purpose of these experiments was to gain further mechanistic insight into the trainability of rFE and rFD, on the basis of serial sarcomere number (SSN) alterations to length-dependent properties. Maximal rFE/rFD measures from the soleus and extensor digitorum longus (EDL) of rats were compared after 4 weeks of uphill/downhill running and a no running control. Serial sarcomere numbers adapted to the training: soleus serial sarcomere number was greater with downhill compared to uphill running, while EDL demonstrated a trend towards more serial sarcomeres for downhill compared to no running. In contrast, absolute and normalized rFE/rFD did not differ across training groups for either muscle. As such, it appears that training-induced SSN adaptations do not modify rFE/rFD at the whole-muscle level. Summary Statement The addition and subtraction of serial sarcomeres induced by downhill and uphill running, respectively, did not influence the magnitude of stretch-induced force enhancement and shortening-induced force depression.
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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 | 0.000 |
| 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".