Coexistence of potentiation and low-frequency fatigue during voluntary exercise in human skeletal muscle
Bibliographic record
Abstract
The role of muscle potentiation in overcoming low-frequency fatigue (LFF) as it developed during submaximal voluntary exercise was investigated in eight males (age 26.4 +/- 0.7 years, mean +/- SE) performing isometric leg extension at approximately 30% of maximal voluntary contraction for 60 min using a 0.5-duty cycle (1 s contraction, 1 s rest). At 5, 20, 40, and 60 min, exercise was interrupted for 3 min, and the maximum positive rate of force development (+dF/dtmax) and maximal twitch force (Pt) were measured in maximal twitch contractions at 0, 1, 2, and 3 min of rest (R0, R1, R2, R3); they were also measured at 15 min of recovery following the entire 60-min exercise period. These measures were compared with pre-exercise (PRE) as an indicator of potentiation. Force at low frequency (10 Hz) was also measured at R0, R1, R2, and R3, and at 15 min of recovery, while force at high frequency (100 Hz) was measured only at R0 and R3 and in recovery. Voluntary exercise increased twitch +dF/dtmax at R0 following 5, 20, 40, and 60 min of exercise, from 2553 +/- 150 N/s at PRE to 39%, 41%, 42%, and 36% above PRE, respectively (P<0.005). Twitch +dF/dtmax decayed at brief rest (R3) following 20, 40, and 60 min of exercise (P<0.05). Pt at R0 following 5 and 20 min of exercise was above that at PRE (P<0.05), indicating that during the early phase of moderate-intensity repetitive exercise, potentiation occurs in the relative absence of LFF. At 40 and 60 min of exercise, Pt at R0 was unchanged from PRE. The LFF (10 Hz) induced by the protocol was evident at 40 and 60 min (R0-R3; P<0.05) and at 15 min following exercise (P<0.05). High-frequency force was not significantly compromised by the protocol. Since twitch force was maintained, these results suggest that as exercise progresses, LFF develops, which can be compensated for by potentiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".