Conflicting Effects of Fatigue and Potentiation on Voluntary Force
Bibliographic record
Abstract
The objective of this study was to investigate whether a warm-up consisting of a series of maximal contractions would augment the force and activation of subsequent leg extensor contractions. Both voluntary and evoked isometric contractions were tested to determine the mechanisms underlying the response. Nine subjects were tested for twitch, tetanic, submaximal (30%), and maximal voluntary contractile (MVC) properties before and after (1, 5, 10, and 15 minutes) one to three 10-second MVCs. MVC force either did not change following 1-2 MVCs or was depressed at 10 and 15 minutes after 3 MVCs. MVC activation was decreased (4.4-6.9%) throughout recovery, whereas submaximal contractions were minimally affected. Although overall, twitches were potentiated (15.5-19.8%) posttest, 3 MVCs had significantly greater twitch potentiation than 1 or 2 MVCs at 5 and 10 minutes. Results suggest that voluntary and evoked contractions respond differently to prior 10-second MVCs. In the present study, a warm-up routine of 1-3 MVCs of a 10-second duration did not enhance subsequent voluntary performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".