Bibliographic record
Abstract
When searching for the term "muscle power" on Google Scholar, about 3.7 million hits come up in 60 ms, and for the past 3 years, there were approximately 225 yearly peer-reviewed publications dealing with muscle power.Muscle power has been used to assess and predict athletic performance, to determine muscle rehabilitation following injury or disease, to measure functional decline as occurs in aging, and many other topics.In 1984, Alan McComas and colleagues from McMaster University organized a conference entitled "human muscle power".It was the first muscle conference I (Walter Herzog) attended, and probably the first that dealt with muscle power exclusively.The distinct memory that remains from that conference is the confusion the term "muscle power" generated, a confusion that was caused by scientists from different disciplines interpreting power in different ways and rarely defining it.Advancing 40 years, Ronei Pinto and colleagues organized an international symposium on muscle power at the Federal University of Rio Grande do Sul in 2024.Like for the first conference 40 years earlier, muscle researchers from a variety of fields, including biomechanics, physiology, strength and conditioning, biochemistry, and energetics, were represented.Once again, the use of the term "muscle power" differed from one field to another, and few speakers defined it.Moreover, muscle power was associated with performance criteria such as the height achieved in a vertical jump or the change in kinetic energy and linear momentum of a system, criteria that do not depend on, and do not relate to, muscle power in a causal (mechanical) manner, resulting in misunderstandings between researchers across disciplines.The purpose of this paper is to clarify what "mechanical power" is, how it is defined, and how it may be used in the proper physical sense in muscle mechanics.This definition may then be a guide for how the term "muscle power" may be/ should be used to avoid further confusion.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.060 |
| Scholarly communication | 0.014 | 0.047 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.015 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".