Concurrent Fatigue and Potentiation in Endurance Athletes
Bibliographic record
Abstract
PURPOSE: Countermovement jump (CMJ) and maximum running speed over a distance of 20 m were evaluated for examination of the concurrent fatigue and postactivation potentiation (PAP) in endurance athletes after an incremental field running test. METHODS: Twenty-two endurance athletes performed two attempts of CMJ on a force plate and maximum running speed test before and following the Université de Montréal Track Test (UMTT). RESULTS: The results showed an improvement in CMJ height (3.6%) after UMTT that correlated with the increment in peak power (3.4%), with a concurrent peak force loss (-10.8%) that correlated with peak power enhancement. The athletes maintained their 20 m sprint performance after exhaustion. Cluster analysis reinforced the association between CMJ and peak power increments in responders with a reported correlation between peak power and sprint performance increments (r = .623; P = .041); nonresponders showed an impairment of peak force, vertical stiffness, and a higher vertical displacement of the center of mass during the countermovement that correlated with lactate concentration (r = -0.717; P = .02). CONCLUSIONS: It can be suggested that PAP could counteract the peak force loss after exhaustion, allowing the enhancement of CMJ performance and the maintenance of sprint ability in endurance athletes after the UMTT. From these results, the evaluation of CMJ after incremental running tests for the assessment of muscular adaptations in endurance athletes can be recommended.
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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".