Perceived risk for falls and decision-making in riding raised ramps in mountain biking: a pilot study
Bibliographic record
Abstract
Mountain biking (MTB) is a challenging activity where riders face constant decisions on whether to attempt technical paths or features (e.g., wooden ramps and jumps) that pose risk for falls and injuries. Risk homeostasis theory posits that riders pursue an optimal non-zero level of risk that balances the rewards of attempting challenging features with the need to avoid unreasonable risk for injury. Little is known on how riders judge risk, and the level of risk that riders deem unacceptable. We conducted experiments with experienced MTB riders (n = 17) to examine how their willingness to ride raised wooden ramps depended on their perceived probability for falling (Pf) and their perceived probability for injury in the event of a fall (Pi) while riding the ramp. In one experiment, participants viewed ramps of varying widths and heights and described their willingness to ride each ramp, along with Pf and Pi. We found that Pf and Pi were independent predictors of willingness to attempt ramps. Moreover, the product Pf*Pi (the perceived risk for injury in attempting the ramp) was a stronger predictor than Pf or Pi alone. In a second experiment, participants viewed ramps of different widths, and reported the maximum (threshold) height where they would ride each ramp, along with Pf and Pi. We found that Pf*Pi at the threshold height, averaging 13%, did not vary with ramp width. We conclude that decisions on riding ramps are based on the product Pf*Pi. On average, riders refused to ride ramps when Pf*Pi exceeded 13%.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".