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Record W2571871584 · doi:10.1089/ham.2016.0135

Free Flight Physiology: Paragliding and the Study of Extreme Altitude

2017· article· en· W2571871584 on OpenAlexaff
Matt Wilkes, Martin J. MacInnis, Matthew J. Witt, Michael Vergalla, Mathieu Verschave Keysers, Adrian L. R. Thomas, Lucy A. Hawkes

Bibliographic record

VenueHigh Altitude Medicine & Biology · 2017
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKingdomMountaineeringAltitude (triangle)Library scienceHistoryBiologyComputer scienceArchaeologyMathematics

Abstract

fetched live from OpenAlex

Aims We sought to describe the physiological demands and the impact of environmental stressors of paragliding, a popular and evolving form of free flight, at moderate and extreme altitudes. We recorded oxygen consumption (VO2), heart rate (HR), respiratory frequency (fR), tidal volume (VT), oxygen saturation, accelerometry (G) and altitude in eight male pilots: 9.3 hours of flight at moderate altitudes (to 3,073 m, n=4), 19.3 hours at extreme altitude (to 7,458 m, n=2) and during high-G manoeuvers (n=2). We also analysed heart rate data from 17 male pilots (138 hours). Results Overall energy expenditure at moderate altitude was low (1.7 (0.6) metabolic equivalents) but physiological parameters were notably higher during take-off (p < 0.05). Pilots transiently reached ~7 G during manoeuvres. Mean HR at extreme altitude (112 (14) bpm) were elevated compared to moderate altitude (98 (15) bpm, p = 0.048). While VT were similar (p = 0.958), elevation in fR at extreme compared to moderate altitude approached significance (p = 0.058). Conclusions Physical exertion in paragliding appears low, so 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.344
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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