The Effects of Different Rest Interval Lengths on Acute Quarter-squat Performance in Female
Bibliographic record
Abstract
The inclusion of resistance training in the athletic preparation of young athletes is associated with increased time and personal demands. This study compared the effect of different rest interval (RI) lengths on quarter-squat performance in two age groups. Sixteen girls (age 12.20 ± 0.55) and 16 women (age 23.13 ± 2.23) performed three series of 10 quarter squats (10 repetition maximum [RM] load) with different RI times (1 min, 2 min, 3 min) between the three series. Each participant was randomly tested under all the RI conditions. The number of successful repetitions, power, and speed were recorded for each set using by FitroDyne device. The women completed significantly less repetitions in the third set than in the first set for the 2-min and 1-min RIs (9.38 ± 1.54, p = .05 and 8.44 ± 2.42; p = .003, respectively). There was observed a significant decrease in mean power and speed in the 3-min RI between the first and second set and in the 1-min RI between the first and third sets. No significant differences in numbers of completed repetitions and mean power were evident in girls for any RI condition, but there was a significant decrease in mean velocity in the 2-min RI between the first and second sets in this group. These results show that recommendations for adults may be not suitable for girls; the girls’ performance in three sets of 10 quarter squats was less affected by RI than the women’s performance.
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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) | 0.002 | 0.001 |
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".