Quantifying Risk in Air Sports: Flying Activity and Incident Rates in Paragliding
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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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".