Fatigue and recovery of dynamic and steady-state performance in frog skeletal muscle
Bibliographic record
Abstract
Muscle fatigue reflects alterations of both activation and cross-bridge function, which will have markedly different affects on steady-state vs. dynamic performance. Such differences offer insight into the specific origins of fatigue, its mechanical manifestation, and its consequences for animal movement. These were inferred using dynamic contractions (twitches and cyclic work as might occur during locomotion) and steady-state performance with maximal, sustained activation (tetani, stiffness, and isokinetic force) during fatigue and then recovery of frog (Rana pipiens) anterior tibialis muscle. Stiffness remained unaltered during early fatigue of force and then declined only 25% as force dropped 50%, suggesting a decline with fatigue in first the force-generating ability and then the number of cross bridges. The relationship between stiffness and force was different during fatigue and recovery; thus the number of cross bridges and force per cross bridge are not intimately linked. Twitch duration increased with fatigue and then recovered, with trajectories that were remarkably similar to and linear with changes in tetanic force, perhaps belying a common mechanism. Twitch force increased and then returned to resting levels during fatigue, reflecting a slowing of activation kinetics and a decline in cross-bridge number and force. Net cyclic work fatigued to the degree of becoming negative when tetanic force had declined only 15%. Steady-state isokinetic force (i.e., shortening work) declined by 75%, while cyclic shortening work declined only 30%. Slowed activation kinetics were again responsible, augmenting cyclic shortening work but greatly augmenting lengthening work (reducing net work). Steady-state measures can thus seriously mislead regarding muscle performance in an animal during fatigue.
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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".