The Influence of the Self-Contained Breathing Apparatus (SCBA) on Ventilatory Function and Maximal Exercise
Bibliographic record
Abstract
Our previous work showed that breathing low density gases during exercise with the self-contained breathing apparatus (SCBA) improves maximal ventilation (V(E)) and maximal oxygen consumption (VO(2)max). This suggests that the SCBA limits exercise by adding a resistive load to breathing. In this study we compared VO(2)max with and without the various components comprising the SCBA to determine their impact on VO(2)max. Twelve males performed 4 randomly ordered incremental exercise tests to exhaustion on a treadmill: (1) low-resistance breathing valve only (CON); (2) full SCBA (SCBA); (3) SCBA regulator only (REG); and (4) carrying the cylinder and harness assembly but breathing through a low-resistance breathing valve (PACK). Compared to CON, VO(2)max was reduced to a similar extent in the SCBA and REG trials (14.9% and 13.1%, respectively). The PACK condition also reduced VO(2)max, but to a lesser extent (4.8 +/- 5.3%). At VO(2)max, VE was decreased and expiratory mouth pressure and external breathing resistance (BR) were increased in both the SCBA and REG trials. There was a significant correlation between the change in maximal V(E)and VO(2)max with the SCBA. The results show that the SCBA reduces VO(2)max by limiting V(E) secondary to the increased BR of the SCBA regulator.
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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".