Quantifying Risk in Air Sports: Flying Activity and Incident Rates in Paragliding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The volume, nature, and risks of paragliding are poorly quantified. More comprehensive understanding, including incident rates allowing comparison to similar disciplines, will help direct and appraise safety interventions. METHODS: Paraglider pilots were surveyed regarding experience, incidents, recordkeeping, and risk perception. The survey could not capture those who had left the sport or died, so a subset of responses from UK pilots was compared to records from an incident database. RESULTS: There were 1788 (25%) responses from 7262 surveyed. Respondents flew a total of 87,909 h in 96,042 flights during 2019. Local flying was most frequent (n=37,680 flights, 39%) but a higher proportion of hours were spent flying cross-country (n=33,933 h, 39%). The remainder were spent in competition, hike and fly, tandem, aerobatic, or instructional flight. Flying incidents led to 103 (6%) respondents seeking medical attention, attending hospital, or missing a day of work in 2019. Near misses were reported by 423 (26%) pilots. Asymmetry and rotational forces typically led to incidents, and limb and back injuries resulted. Pilots frequently failed to throw their reserve parachutes. Only 3 (0.6%) incidents involved equipment failure, with the remainder attributed to control or decision errors. Incident rates of paragliding were estimated as 1.4 (1.1-1.9) deaths and 20 (18-27) serious injuries per 100,000 flights, approximately twice as risky as general aviation and skydiving. CONCLUSIONS: Incidents usually resulted from pilot error (control and decision), rather than equipment failure. Future safety interventions should focus on improving glider control skills and encouraging reserve parachute deployment.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it