Effect of prior heavy exercise on O2 uptake kinetics in the upper regions of the moderate‐intensity domain
Bibliographic record
Abstract
That pulmonary O 2 uptake (VO 2 ) kinetics in the upper region of the moderate‐intensity exercise domain are slow relative to the lower region is thought to reflect an intrinsic characteristic of the recruited muscles (Brittain et al., EJAP 86:125, 2001). Because prior heavy exercise speeds moderate‐intensity VO 2 kinetics when they are slow, we wished to determine whether prior heavy exercise would similarly speed VO 2 kinetics in the upper moderate‐intensity domain. Ten males performed 4–6 repeats of two double‐step (S) cycle ergometer protocols: 1) S1‐S2: 20 W to 80% of the estimated lactate threshold made in two equal increments, and 2) HS1‐HS2: as protocol 1, but with S2 preceded by heavy exercise (Δ50%); each step lasted 6 min. VO 2 was measured breath‐by‐breath using a turbine and mass spectrometer. The fundamental VO 2 time constant (τ) was greater (p<0.05) in S2 (41 ± 15 s) than S1 (21 ± 4 s). After heavy exercise, τVO 2 was lower (p<0.05) in HS2 (31 ± 10 s) than S2, but remained greater (p<0.05) than HS1 (22 ± 5 s). Thus VO 2 kinetics in the upper moderate‐intensity domain are not immutable, becoming faster after heavy exercise. However, this effect (presumably due to activation of intramuscular enzymes, improved oxidative substrate provision and/or improved O 2 transport kinetics) did not entirely overcome the intrinsically‐slow kinetics in this region. Supported by NSERC, Canada; Wellcome Trust, UK
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".