Effects of fractional inspired O <sub>2</sub> on the O <sub>2</sub> pathway during submaximal and maximal exercise in male endurance athletes
Bibliographic record
Abstract
Abstract We utilized non‐invasive methods and novel computational approaches to examine the effects of acutely varying fractional inspired O 2 (FIO 2 ) on convective and diffusive steps of O 2 transport and muscle tissue (de)oxygenation during incremental cycling to exhaustion in 10 Tier 3 and 4 endurance athletes breathing either 0.152, 0.209, or 0.298 FIO 2 . At submaximal work rates (100–275 W) in hypoxia, higher cardiac output compensated for lower arterial O 2 content. At maximal work rate, convective O 2 transport was lower in hypoxia (mean [95%CI]: 5.37 [5.14–5.59] L/min, q < 0.0001) and higher in hyperoxia (6.84 [6.50–7.18] L/min, q = 0.043) compared to normoxia (6.56 [6.16–6.95] L/min), whereas O 2 diffusive conductance did not differ between conditions (94 [82–106], 98 [83–112], 98[87–109] mL/min/mmHg for hypoxia, normoxia and hyperoxia, respectively, p = 0.490). Consequently, maximal O 2 uptake (V̇O 2max ) was lower in hypoxia (4.06 [3.85–4.27] L/min, q < 0.0001) and higher in hyperoxia (5.02 [4.85–5.19] L/min, q = 0.003) compared to normoxia (4.83 [4.63–5.02] L/min). In hypoxia, muscle tissue saturation index was 1%–4%‐units lower compared to normoxia and hyperoxia during submaximal cycling but similar at maximal work rate. In summary, central and peripheral compensatory mechanisms maintained O 2 uptake despite altered FIO 2 at submaximal work rates. At maximal work rate the effects of hypoxia and hyperoxia on V̇O 2max were mediated through convective O 2 transport.
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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) | 0.000 | 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".