Effects of short‐term training and detraining on<scp><scp>VO</scp></scp><sub>2</sub>kinetics: Faster<scp><scp>VO</scp></scp><sub>2</sub>kinetics response after one training session
Bibliographic record
Abstract
This study examined the time course of short-term training and detraining-induced changes in oxygen uptake ( V ˙ O 2 ) kinetics. Twelve men (24 ± 3 years) were assigned to either a 50% or a 70% of V ˙ O 2 m a x training intensity (n = 6 per group). V ˙ O 2 was measured breath-by-breath. Changes in deoxygenated-hemoglobin concentration (Δ[HHb]) were measured by near-infrared spectroscopy. Moderate-intensity exercise on-transient V ˙ O 2 and Δ[HHb] were modeled with a mono-exponential and normalized (0-100% of response) and the [ H H b ] / V ˙ O 2 ratio was calculated. Similar changes in time constant of V ˙ O 2 ( t V ˙ O 2 ) were observed in both groups. The combined group mean for t V ˙ O 2 decreased ∼14% (32.3 to 27.9 s, P < 0.05) after one training session with a further ∼11% decrease (27.9 to 24.8 s, P < 0.05) following two training sessions. The t V ˙ O 2 p remained unchanged throughout the remaining of training and detraining. A significant "overshoot" in the [ H H b ] / V ˙ O 2 ratio was decreased (albeit not significant) after one training session, and abolished (P < 0.05) after the second one, with no overshoot observed thereafter. Speeding of V ˙ O 2 kinetics was remarkably quick with no further changes being observed with continuous training or during detraining. Improve matching of local O2 delivery to O2 utilization is a mechanism proposed to influence this response.
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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".