Considerations for determining the time course of post-activation potentiation
Bibliographic record
Abstract
We sought to determine the efficacy of using a continuous time course trial to assess the temporal profile of post-activation potentiation and to determine the time course of potentiation of discrete jump squat kinetic and kinematic variables. Eight physically trained men performed jump squats before and 4, 8, and 12 min after a 5-repetition maximum back squat. Time intervals were assessed in 3 discontinuous trials (each time interval assessed in a separate trial) and in 1 continuous trial (all time intervals assessed in a single trial). Percentage differences between continuous and discontinuous trials at each time interval were mostly insubstantial. Discrete variables displayed a diverse time course (effect size: trivial to large); time to maximal values ranged between 5.00 ± 2.53 min (concentric peak force) and 9.50 ± 2.98 min (eccentric mean force). Eccentric variables (8.58 ± 3.56 min) took longer to peak than concentric variables (6.64 ± 2.93 min) (effect size: small). Individual subjects attained maximal values for kinetic and kinematic variables at different time intervals, yet the 4-min interval typically displayed the greatest magnitude and frequency of potentiation. We conclude that a continuous time course trial does not substantially influence performance of subsequent jumps and is appropriate for determining the temporal profile of potentiation, which is influenced by discrete jump squat kinetic and kinematic variables and individual differences.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".