Respiratory Muscle Power and the Slow Component of O2 Uptake
Bibliographic record
Abstract
PURPOSE: The slow component of O2 uptake (V˙O2sc) represents a progressive decline in work efficiency during strenuous, constant work rate cycling. Although most of this "excess" O2 uptake can be explained by factors intrinsic to the exercising muscles, it has been proposed that respiratory muscle work rate may also contribute to the V˙O2sc response. To date, however, no study has provided a comprehensive analysis of the mechanical power of breathing (Pb) in relation to the V˙O2sc while performing strenuous exercise. METHODS: The mechanical Pb was measured in 12 recreational cyclists (24 ± 1 yr, 70.9 ± 3.9 kg) during heavy- and severe-intensity cycling. The resistive and elastic components of Pb were quantified using the modified Campbell diagram. RESULTS: Total Pb significantly increased (P < 0.05) over the V˙O2sc phase during both heavy (Δ13 ± 2 J·min) and severe work rate transitions (Δ219 ± 56 J·min). The magnitude of this increase was relatively greater during severe cycling trials (P < 0.05). The greater "slow component" rise in total Pb during severe work rate transitions was consequent to relatively larger increases in the inspiratory and expiratory resistive Pb and inspiratory elastic Pb (P < 0.05). CONCLUSIONS: The findings of this investigation support the thesis that the energetic contribution from respiratory muscles to the V˙O2sc amplitude is disproportionately higher during severe- compared with that during heavy-intensity 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) | 0.002 | 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".