Utilization of recommended safe-landing strategies during falls in mountain biking
Bibliographic record
Abstract
Falls are common in mountain biking (MTB), and often involve high speeds, large descent heights, and rough landing terrains. However, most falls in MTB do not cause serious injury. This may be due, in part, to protective movements used by MTB riders to avoid injury. Such "safe-landing strategies" are commonly discussed in the MTB community. However, studies have not synthesized or examined the validity of the recommended strategies. Our goal in this study was to determine whether riders utilize recommended safe-landing strategies during real-life falls in MTB. To address this goal, we identified 11 recommended safe-landing strategies through online content analysis and experienced MTB rider surveys. We then analyzed videos of 300 real-life MTB falls using a structured questionnaire to determine whether riders utilized the recommended strategies. The most commonly used strategies were upper limb bracing (58.3 %), elbow flexion at landing (48.0 %), stepping (47.0 %), knee flexion at landing (43.0 %), and bike separation (40.0 %). The least utilized strategies were reach-to-grasp (4.7 %), use of the arms to shield the face (6.3 %) and dismounting from the bike (6.7 %). Moderately utilized strategies included body rolling (26.7 %), neck rotation (26.7 %), and tucking (18.3 %). In 96 % of falls, rider utilized at least 1 recommended landing strategy. On average, riders utilized 3.04 (SD 1.6) recommended landing strategies when falling. Our results indicate that falls in MTB elicit common movement strategies that align with recommended techniques for avoiding injury during falls. Future research should examine the role of exercise in enhancing safe-landing responses and preventing injuries in MTB.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".