Response to the Comment on “A New Taxonomy for Postactivation Potentiation in Sport”
Bibliographic record
Abstract
We thank Dr Smith and Professor MacIntosh for the opportunity to further discuss the implications of the new proposed taxonomy. In their letter, 1 they claim that the definition they propose is in contrast with that cited in our article 2 and argue that, while their definition does not stipulate a mechanism, our definition does so. Honestly, we find it challenging to distinguish between the 2 definitions. When comparing the terminology, we see quite similar nomenclature and no mechanisms proposed. Furthermore, our definition does not differ substantially from prior classical definitions. mith and MacIntosh state: "This is an important point because Boullosa et al justify their commentary based on assumed mechanisms." 1 However, it has been ubiquitously agreed since the pioneering works in the 80s that the mechanisms for postactivation potentiation (PAP) are well established. In fact, Professor MacIntosh's own impressive work has helped to define these mechanisms. Hence, the literature has consistently agreed upon the mechanisms of PAP over the last 30 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".