Repeated-Sprint Ability and Aerobic Fitness
Bibliographic record
Abstract
The purpose of this study was to reinvestigate the relationship between aerobic fitness and fatigue indices of repeated-sprint ability (RSA), with special attention to methodological normalization. Soldiers were divided into low (n = 10) and high (n = 9) fitness groups according to a preset maximal aerobic speed (MAS) of 17 km·h(-1) (∼60 ml O2·kg(-1)·min) measured with the University of Montreal Track Test (UMTT). Subjects' assessment included the RSA test (3 sets of 5 40-m sprints with 1-minute rest between sprints and 1.5 minutes between sets), a 40-m sprint (criterion test used in the computation of fatigue indices for the RSA test), strength and power measurement of the lower limbs, and the 20-m shuttle run test (20-m SRT) and the UMTT, which are measures of maximal aerobic power. The highest correlation with the RSA fatigue indices was obtained with the 20-m SRT (r = 0.90, p = 0.0001, n = 19), a test with 180° direction changes and accelerations and decelerations. The lower correlation (r = 0.66, p < 0.01, n = 19) with the UMTT (continuous forward running) suggests that some aerobic tests better disclose the importance of aerobic fitness for RSA and that aerobic power is not the sole determinant of RSA. However, neither strength nor vertical jumping power was correlated to the RSA fatigue indices. Subjects with greater MAS were able to maintain almost constant level of speed throughout series of repeated sprints and achieved better recovery between series. A MAS of at least 17 km·h(-1) favors constant and high speed level during repeated sprints. From a practical point of view, a high aerobic fitness is a precious asset in counteracting fatigue in sports with numerous sprint repetitions.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
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".