The effects of priming exercise on the <i>V</i>O<sub>2</sub> slow component and the time-course of muscle fatigue during very-heavy-intensity exercise in humans
Bibliographic record
Abstract
We hypothesized that prior exercise would attenuate the muscle fatigue accompanied by oxygen uptake slow-component (V̇O2SC) behavior during a subsequent very-heavy (VH)-intensity cycling exercise. Thirteen healthy male subjects performed tests to determine the critical power (CP) and the fixed amount of work above CP ([Formula: see text]) and performed 6 square-wave bouts until 3 or 8 min, each at a work rate set to deplete 70% [Formula: see text] in 8 min, with a maximal isokinetic effort before and after the conditions without (VHCON) and with prior exercise (VHEXP), to measure the cycling peak torque decrement. The V̇O2SC magnitude at 3 min (VHCON = 0.280 ± 0.234, VHEXP = 0.116 ± 0.109 L·min−1; p = 0.04) and the V̇O2SC trajectory were significantly lower for VHEXP (VHCON = 0.108 ± 0.042, VHEXP = 0.063 ± 0.031 L·min−2; p < 0.01), leading to a V̇O2SC magnitude at the eighth minute that was significantly lower than VHCON (VHCON = 0.626 ± 0.296 L·min−1, VHEXP = 0.337 ± 0.179; p < 0.01). Conversely, peak torque progressively decreased from pre-exercise to 3 min (Δtorque = 21.5 ± 7.7 vs. 19.6 ± 9.2 Nm) and to 8 min (Δtorque = 29.4 ± 15.8 vs. 27.5 ± 12.0 Nm) at VHCON and VHEXP, respectively, without significant differences between conditions. Regardless of the condition, there was a significant relationship between Δtorque and the V̇O2SC (R2: VHCON = 0.23, VHEXP = 0.25; p = 0.01). Considering that “priming” effects on the V̇O2SC were not accompanied by the muscle force behavior, these findings do not support the hypothesis of a “causal” relationship between the time-course of muscle fatigue and V̇O2SC.
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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 | 0.000 |
| 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".