Easiest Routes and Slow Zones: How Fast Do I Go?: Speeds and Distances of Recreational and Expert Snowsport Participants
Bibliographic record
Abstract
Abstract High speeds in snowsports have been associated with both the affective appeal as well as the risk of injury. Previous research of speeds of snowsport participants have been recorded on limited terrain or a single run using static radar guns or speed cameras. However, from a resort design and management perspective, more information is needed about areas of potential risk where there are a variety of users, skill levels, and speed. This exploratory research seeks to understand the actual and perceived distance and speeds traveled by a variety of snowsport participants over their day’s participation as well in resort-designated “slow zones.” A convenience sample of expert and recreational participants was recruited in a Western Canadian resort during the 2010–2011 season. A GPS-based data-logging device recorded speed, distance, duration, and location. Participants completed a questionnaire covering demographics, perceptions of maximum speed and distance traveled, and recommended speeds in slow zones. Data was collected over 102 sessions for alpine skiers, snowboarders, and telemarkers who traveled >4.5 km during their data-collection period: age range 9–80 years (x¯ = 42.0), 39.8 % females and 67.6 % advanced/expert. Total skiing/boarding time logged was 497 h (17 min–7 h, 38 min, x¯ = 4 h, 52 min) covering 4475 km (x¯ = 43.87 km). Estimates of distance traveled was 3–100 km (x¯ = 33.70 km, SD = 21.98 km). Maximum speeds recorded were 20.2–108.5 km/h (x¯ = 62.06 km/h); all but two recorded maximum speeds >23 km/h. Estimated maximum speeds ranged from 1–100 km/h (x¯ = 50.82 km/h). A paired sample t-test of estimated and actual maximum speeds was significant (p = .000). Participants’ recommendation for speeds in slow zones ranged from 5 to 60 km/h (x¯ = 23.8 km/h, mode = 30 km/h). Participants were generally unaware of the distances they traveled and the maximum speeds achieved, with most traveling in slow zones at speeds greater than their own recommendations.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".