Force–frequency relationship during fatiguing contractions of rat medial gastrocnemius muscle
Bibliographic record
Abstract
The force-frequency relationship presents the amount of force a muscle can produce as a function of the frequency of activation. During repetitive muscular contractions, fatigue and potentiation may both impact the resultant contractile response. However, both the apparent fatigue observed, and the potential for activity-dependent potentiation can be affected by the frequency of activation. Thus, we wanted to explore the effects that repetitive stimulation had on the force-frequency relationship. The force-frequency relationship of the rat medial gastrocnemius muscle was investigated during consecutive bouts of increasing fatigue with 20 to 100 Hz stimulation. Force was measured prior to the fatiguing protocol, during each of three levels of fatigue, and after 30 min of recovery. Force at each frequency was quantified relative to the pre-fatigued 100 Hz contractions, as well as the percentage reduction of force from the pre-fatigued level at a given frequency. We observed less reduction in force at low frequencies compared to high frequencies, suggesting an interplay of fatigue and potentiation, in which potentiation can "protect" against fatigue in a frequency-dependent manner. The exact mechanism of fatigue is unknown, however the substantial reduction of force at high frequency suggests a role for reduced force per cross-bridge.
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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) | 0.001 | 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".