Voluntary activation in the triceps brachii at short and long muscle lengths
Bibliographic record
Abstract
The aim was of this study was to determine whether voluntary activation calculated using the interpolated twitch technique (ITT) would be underestimated by muscle length-induced changes in the twitch amplitude evoked at rest after maximum voluntary contraction (MVC) in the elbow extensors. In 12 healthy men, calculated voluntary activations were compared at short (20 degrees of elbow flexion) and long muscle lengths (120 degrees ) using the actual post-MVC doublet, and the predicted post-MVC doublet estimated from linear or nonlinear extrapolations. Actual post-MVC doublet amplitudes were smaller at 20 degrees versus 120 degrees . At 20 degrees , the predicted post-MVC doublet obtained from nonlinear extrapolation was larger, and voluntary activation values improved by 5-33% at the submaximal voluntary contraction intensities (< or =80% of MVC). When voluntary drive is compromised, this method of extrapolation is useful to account for mechanical limitations that blunt the actual post-MVC doublet, which otherwise leads to underestimation of voluntary activation.
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 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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".