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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".