Effects Of Oxygen Breathing On Wingate Test Parameters
Bibliographic record
Abstract
It is well established in the literature that oxygen breathing during intense aerobic exercise improves performance. However, the magnitude of performance enhancement cannot be completely explained by metabolic mechanisms and therefore may involve central and peripheral neural factors. PURPOSE To examine the potential effect of O2-breathing during a predominantly anaerobic test. METHODS Twenty active male and female subjects (18–25yrs), completed a series of 4 Wingate tests using a Monark 894E computerized system. Two tests were completed while breathing 21% O2 and two breathing 100% O2. Each test was preceded by a 10 min breathing period. All tests were randomly assigned, single-blind with a least a day between tests. Measurements of peak power, minimum power, average power and power drop (%) were collected. In addition, VCO2 was collected during the test and for 10min recovery in order to estimate aerobic metabolism. RESULTS Peak power in watts (W) was attained for all tests during the first 5sec. 60% of subjects achieved max. peak power during hyperoxia. Peak power was 2.9% greater for hyperoxia (803 W) than normoxia (780 W) and average power was 2.5% greater for hyperoxia (611 W) than normoxia (596 W). Average power drop was similar between test conditions: during the first 15 sec. (normoxia=11.8%, hyperoxia=11.6%) and for the 30 sec. duration of the test (normoxia=39.8%, hyperoxia=38.7%). Total VCO2 was highest in all but 2 subjects during hyperoxia, suggesting a slight increase in aerobic metabolism. Following test completion subjects' perceived exertion was consistently less in hyperoxia than normoxia. CONCLUSION The data suggest that O2 breathing has minimal effect on work performance parameters measured during a primarily anaerobic test.
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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 | 0.002 |
| 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".