Relationships between <i>V</i>O<sub>2</sub> and blood lactate responses after all-out running exercise
Bibliographic record
Abstract
To verify the effects of training status and blood lactate concentration (BLC) responses on the early excess postexercise oxygen consumption (EPOC), 8 sprinters, 7 endurance runners, and 7 untrained subjects performed an incremental test to determine maximal oxygen uptake and a 1-min all-out test to determine BLC and oxygen uptake recovery curves. BLC kinetics was evaluated to assess the quantity of lactate accumulated during exercise (QlaA), lactate removal ability (k2), and quantity of lactate removed from 0 to 10 min postexercise (QlaR). Oxygen uptake off-kinetics was evaluated to assess the decay time constants (τ1 and τ2); moreover, EPOC was measured during the first 10 min after exercise. While sprinters had 98%-100% and 94%-100% likelihood of having the highest EPOC and decay time constants, endurance runners had 98%-100% and 95%-100% likelihood of having the lowest EPOC and decay time constants. EPOC was correlated with QlaA (r = 0.74) and QlaR (r = 0.61). τ1 and τ2 were correlated with maximal oxygen uptake (r > -0.57), k2 (r > -0.48), and QlaR relative to QlaA (r > -0.60). Our findings indicate that oxygen uptake recovery is associated with fast lactate removal and aerobic training. Furthermore, the metabolites derived from anaerobic energy production seem to induce a greater EPOC after all-out exercise.
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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".