The Application of Postactivation Potentiation to Elite Sport
Bibliographic record
Abstract
Recently there has been considerable interest and research into the functional significance of postactivation potentiation (PAP) on sport performance. The interest has evolved around the potential for enhancing acute performance or the long-term training effect, typically in the form of complex training. Complex training usually involves performing a weight-training exercise with high loads before executing a plyometric exercise with similar biomechanical demands. Despite a considerable amount of research in the past 10 years it would seem there is still much research to be done to fully determine whether PAP has a functional role and, if so, how to best exploit it. It is clear from the research that there are many factors that need to be considered when attempting to apply PAP to an athlete. It is possible that a well-conceived sport-specific warm-up might be as or more effective in enhancing acute performance and easier to apply in a practical setting. In addition, despite its current popularity, there has not been 1 study that has effectively examined the efficacy of complex training and whether it has any advantage over other forms of training that combine weight training and plyometrics but not in the same training session.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".