Training Distribution And The Acquisition Of Maximal Isometric Elbow Flexion Strength
Bibliographic record
Abstract
Twenty-six sedentary, college-aged females were matched and randomly assigned to one of two groups. The massed group (n=13) completed 15 maximal isometric elbow flexion strength trials in one session, while the distributed group (n=13) performed five such contractions on three successive days. After a two-week and three month rest interval, both groups returned to perfonn another five maximal isometric elbow flexion strength trials to assess retention of any potential strength gains. Elbow flexion torque and surface electromyography (SEMG) of the biceps and triceps were monitored concurrently. There was a significant (P < 0.05) increase in strength in both groups from block one (first five contractions) to block four (first retest) and from block one to block five (second retest). Both groups exhibited a similar linear increasing (P < 0.05) trend in biceps root-mean-square (RMS) SEMG amplitude. A significant (P < 0.05) decrease in triceps RMS SEMG amplitude was found between block one and block four for the distributed group. However, a significant (P < 0.05) increase was then found between block one and five for the massed group, and between blocks four and five for distributed group. These results suggest that there is flexibility in resistive exercise schedules. An increase in neural drive to the agonist muscle continued throughout testing. This was accompanied by a reduction in antagonist co activation that was a short-tenn (two weeks) training effect, dissipated over the longer rest interval (three months).
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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".