The effects of hyperoxia on performance during simulated firefighting work
Bibliographic record
Abstract
This study evaluated the effects of hyperoxia (inspired oxygen fraction = 40%) on performance during a simulated firefighting work circuit (SFWC) consisting of five events. On separate days, 17 subjects completed at least three orientation trials followed by two experimental trials while breathing either normoxic (NOX) and hyperoxic (HOX) gas mixtures that were randomly assigned in double-blind, cross-over design. Previously, ventilatory threshold (Tvent) and VO2max had been determined during graded exercise (GXT) on a cycle ergometer. Lactate concentration in venous blood was assessed at exactly 5 min after both the experimental trials and after the GXT. Total time to complete the SFWC was decreased by 4% (p < 0.05) with HOX. No differences were observed in individual event times early in the circuit, however HOX resulted in a 12% improvement (p < 0.05) on the final event. A significantly decreased rating of perceived exertion (RPE) was also recorded immediately prior to the final event. No differences were observed in mean heart rate or post-exercise blood lactate when comparing NOX to HOX. Heart rates during the SFWC (both conditions) were higher than HR at Tvent, but lower than HR at VO2max (p<0.05). Post-SFWC lactate values were higher (p<0.05) than post-VO2max. These results demonstrate that hyperoxia provided a small but significant increase in performance during short duration, high intensity simulated firefighting work.
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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".