Differences in the motor unit firing rates and amplitudes in relation to recruitment thresholds during submaximal contractions of the first dorsal interosseous between chronically resistance-trained and physically active men
Bibliographic record
Abstract
Previous investigations report no changes in motor unit (MU) firing rates during submaximal contractions following resistance training. These investigations did not account for MU recruitment or examine firing rates as a function of recruitment threshold (REC). Therefore, MU recruitment and firing rates in chronically resistance-trained (RT) and physically active controls (CON) were examined. Surface electromyography signals were collected from the first dorsal interosseous during isometric muscle actions at 40% and 70% maximal voluntary contraction (MVC). For each MU, force at REC, mean firing rate (MFR) during the steady force, and MU action potential amplitude (MUAPAMP) were analyzed. For each individual and contraction, the MFRs were linearly regressed against REC, whereas, exponential models were applied to the MFR versus MUAPAMP and MUAPAMP versus REC relationships with the y-intercepts and slopes (linear) and A and B terms (exponential) calculated. For the 40% MVC, the RT had less negative slopes (p = 0.001) and lower y-intercepts (p = 0.006) of the MFR versus REC relationships and lower B terms (p = 0.011) of the MUAPAMP versus REC relationships. There were no differences in either relationship between groups for the 70% MVC. During the 40% MVC, the RT had a smaller range of MFRs and MUAPAMPS in comparison with the CON, likely because of reduced MU recruitment. The RT had lower MFRs and recruitment during the 40% MVC, which may indicate a leftward shift in the force–frequency relationship, and thus require less excitation to the motoneuron pool to match the same relative force.
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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 | 0.000 | 0.001 |
| 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".