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Record W2090121396 · doi:10.1519/r-14653.1

Postactivation Potentiation and Its Practical Applicability: A Brief Review

2005· review· en· W2090121396 on OpenAlexaff
Daniel Robbins

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2005
Typereview
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLong-term potentiationAdaptation (eye)ExploitPsychologyMedicineNeuroscienceIntensive care medicinePhysical medicine and rehabilitationComputer scienceCognitive psychologyInternal medicineComputer security

Abstract

fetched live from OpenAlex

It has been suggested that postactivation potentiation (PAP) may be manipulated to enhance both acute performance and chronic adaptation. PAP refers to the phenomenon by which acute muscle force output is enhanced as a result of contractile history. Evidence exists regarding the existence of PAP. However, the determination of methods to best manipulate and exploit PAP remains elusive. Studies to date would seem to indicate that the practical applicability of PAP in terms of enhancing athletic performance is limited.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.131
GPT teacher head0.468
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations248
Published2005
Admission routes1
Has abstractyes

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