Repeated sprint performance and metabolic recovery curves: effects of aerobic and anaerobic characteristics
Bibliographic record
Abstract
To examine the influence of aerobic and anaerobic indices on repeated sprint (RS) performance and ability (RSA), 8 sprinters (SPR), 8 endurance runners (END), and 8 active participants (ACT) performed the following tests: (i) incremental test; (ii) 1-min test to determine first decay time constant of pulmonary oxygen uptake off-kinetics and parameters related to anaerobic energy supply, lactate exchange, and removal abilities from blood lactate kinetics; and (iii) RS test (ten 35-m sprints, departing every 20 s) to determine best (RSbest) and mean (RSmean) sprint times and percentage of sprint decrement (%Dec). While SPR had a 98%-100% likelihood of having the fastest RSbest (Cohen's d of 1.8 and 1.4 for ACT and END, respectively) and RSmean (2.1 and 0.9 for ACT and END, respectively), END presented a 97%-100% likelihood of having the lowest %Dec (0.9 and 2.2 for ACT and SPR, respectively). RSmean was very largely correlated with RSbest (r=0.85) and moderately correlated with estimates of anaerobic energy supply (r=-0.40 to -0.49). RSmean adjusted for RSbest (which indirectly reflects RSA) was largely correlated with lactate exchange ability (r=0.55). Our results confirm the importance of locomotor- and anaerobic-related variables to RS performance, and highlight the importance of disposal of selected metabolic by-products to RSA.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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".