Neuromuscular changes following simulated high-intensity cycling performance in moderate hypoxia
Bibliographic record
Abstract
The purpose of this study was to determine whether central activation (CA) is reduced following a simulated 20-km cycling time trial (20TT) under normoxic and hypoxic conditions. It was hypothesized that CA, maximal voluntary contraction (MVC), and peripheral variables would become reduced during the 20TT exercise under both normoxic and hypoxic conditions, but to a greater extent under the hypoxic condition. Eight experienced male cyclists performed two simulated 20TTs in random order in a hypoxic chamber at either 15% or 21% fraction of inspired oxygen (FIO 2 ). Using the interpolated twitch technique during MVC of the quadriceps, measurements were collected before the exercise, and at 1, 2, 3, and 4 minutes postexercise. The CA values at 1, 2, 3, and 4 minutes postexercise were all significantly reduced from the pre-exercise value. Significant decreases were also detected in all four postexercise MVC measurements and in the third and fourth peak twitch force (PTF) measurements. All four postexercise MVC measurements were significantly decreased. These findings suggest that CA, MVC, and PTF values were significantly reduced and remain reduced at 4 minutes following a self-paced, simulated endurance cycling performance. However, the hypoxic condition had no effect on CA, MVC, or peripheral variables when compared with the normoxic condition.
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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) | 0.001 | 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".