Postactivation potentiation during voluntary contractions after continued knee extensor task-specific practice
Bibliographic record
Abstract
The purposes of this study were to determine whether performing dynamic conditioning activities (CAs) contributes to postactivation potentiation (PAP); to examine the potential confounding effects of CAs with different velocity, total contraction duration, and total work characteristics; and to gain a greater understanding of potential peripheral and central mechanisms underlying PAP. Voluntary (isokinetic knee extensions at 180°·s(-1)) and electrically evoked torques and electromyogram (EMG) data were captured before and 1, 4, 7, 10, and 13 min after 5 different dynamic CAs (4 knee extensions at 60°·s(-1), 4 and 12 at 180°·s(-1), and 4 and 20 at 300°·s(-1)), after the participants had completed a full warm-up including extensive task-specific practice to the point where maximal voluntary contractile capacity was achieved. Even after maximal voluntary contractile capacity had been achieved, the imposition of CAs of longer total contraction duration (6 s) and a minimum total work of ∼750-900 J elicited significant increases in both voluntary (for 7 min; up to 5.9%) and twitch (for 4 min; up to 13.5%) torques (i.e., PAP), regardless of the velocity of the CA. No changes in EMG:M-wave were detected after any CA. A dynamic voluntary CA can contribute to improved voluntary and electrically evoked torques even when maximal voluntary contractile capacity has previously been achieved. Furthermore, a minimum CA contraction duration and minimum total work appear important to increase torque production, although movement velocity appears unimportant. Changes in peripheral function but not central drive may have contributed to the observed PAP under the present conditions.
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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 | 0.002 |
| 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".