Bibliographic record
Abstract
At the onset of an exercise transition, exponential modeling to calculate a time constant (τ) is the conventional method to analyze pulmonary oxygen uptake (V̇O<sub>2p</sub>) kinetics for moderate and heavy exercises. A new frequency domain analysis technique, mean normalized gain (MNG), has been used to analyze V̇O<sub>2p</sub> kinetics during moderate exercise, but has not been evaluated for its ability to detect differences in kinetics between moderate and heavy exercises. This study tested the hypothesis that MNG would detect smaller amplitude V̇O<sub>2p</sub> responses in the heavy-exercise domain compared with moderate-exercise domain. Eight young healthy adults (3 female; age: 27 ± 6 yr; peak V̇O<sub>2p</sub>: 43 ± 6 mL·min<sup>-1</sup>·kg<sup>-1</sup>; means ± SD) performed three bouts of pseudorandom binary sequence (PRBS) exercise for frequency analysis, with the work rate (WR) changing between 25 W and 90% ventilatory threshold (VT; L → M<sub>PRBS</sub>), 25 W and 50% of the difference between VT and peak V̇O<sub>2p</sub> (Δ50%; L → H<sub>PRBS</sub>), and VT to Δ50% (VT → H<sub>PRBS</sub>). Step exercise tests with equivalent changes in WR to the PRBS tests were performed to facilitate the comparison between MNG and τ. MNG was the highest for L → M<sub>PRBS</sub> (59 ± 7%), then L → H<sub>PRBS</sub> (52 ± 6%), and the lowest for VT → H<sub>PRBS</sub> (38 ± 7%, F<sub>(2,14)</sub> = 129.755, <i>P</i> < 0.001) exercise conditions indicating slower kinetics with increasing exercise intensity that correlated strongly in repeated measures with τ from step transitions (<i>r<sub>rm</sub></i> = -0.893). These results indicate that frequency domain analysis and MNG reliably detect differences in V̇O<sub>2p</sub> kinetics observed across exercise intensity domains.<b>NEW & NOTEWORTHY</b> Mean normalized gain is able to detect differences in V̇O<sub>2p</sub> kinetics between moderate-, heavy-, and heavy-intensity exercises from a raised WR within the same individuals. This new method of kinetic analysis may be advantageous compared with conventional V̇O<sub>2p</sub> curve fitting, as it is less sensitive to breath-by-breath noise, it can provide useful information from a single exercise testing session, and it can be applied to nonconstant work rate exercise situations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".