The Physiology of Paragliding Flight at Moderate and Extreme Altitudes
Bibliographic record
Abstract
Abstract Wilkes, Matt, Martin J. MacInnis, Lucy A. Hawkes, Heather Massey, Clare Eglin, and Michael J. Tipton. The physiology of paragliding flight at moderate and extreme altitudes. High Alt Med Biol 19:42–51, 2018.—Paragliding is a form of free flight, with extreme-altitude paragliding being an emerging discipline. We aimed to describe the physiological demands and the impact of environmental stressors of paragliding at moderate and extreme altitudes. We recorded oxygen consumption (VO 2 ), heart rate (HR), respiratory frequency ( f R), tidal volume ( V T ), oxygen saturation, accelerometry (G), and altitude in 9.3 hours of flight at moderate altitudes (to 3073 m, n = 4), 19.3 hours at extreme altitude (to 7458 m, n = 2), and during high-G maneuvers ( n = 2). We also analyzed HR data from an additional 17 pilots (138 hours) using the Flymaster Live database to corroborate our findings. All pilots were male. Overall energy expenditure at moderate altitude was low [1.7 (0.6) metabolic equivalents], but physiological parameters were notably higher during takeoff ( p < 0.05). Pilots transiently reached ∼7 G during maneuvers. Mean HR at extreme altitude [112 (14) bpm] was elevated compared to moderate altitude [98 (15) bpm, p = 0.048]. Differences in pilots' V T and f R at moderate and extreme altitudes were not statistically significant ( p = 0.96 and p = 0.058, respectively). Thus, we conclude that physical exertion in paragliding is low, suggesting that any subjective fatigue felt by pilots is likely to be cognitive or environmental. Future research should focus on reducing mental workload, enhancing cognitive function, and improving environmental protection.
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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) | 0.001 | 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".