Using Time Lapse Photography to Document Terrain Preferences of Backcountry Skiers
Bibliographic record
Abstract
Avalanches are one of the greatest hazards for those recreating in snow covered mountainous terrain. In the past 20 years an average of 13 people in Canada and 27 people in the US are killed in avalanches each winter. Meanwhile, uncontrolled backcountry avalanche terrain use has significantly increased demonstrated by increased demand for avalanche education and increased sales in backcountry equipment. Lift-accessed backcountry (LABC), or avalanche terrain easily accessed from the ski resort, has seen increased usage since resorts opened boundaries in the mid-1990s. This has led to increased research interest in how people are using backcountry avalanche terrain. A simple method to reduce exposure to avalanche hazard is avoidance, however total avoidance is seldom practical. Professionals and recreational skiers alike mitigate avalanche hazard by managing exposure to terrain containing the avalanche hazard. Current research studies use GPS tracking to study the terrain metrics of backcountry skiers. This GPS research is limited to studying volunteers and professionals that willingly track and submit their trips. This approach ignores many users and thus presents a biased picture of use. This paper develops a method to capture the terrain metrics of all skiers on an avalanche-prone backcountry slope. A remote time-lapse camera focused on a high skier-use backcountry slope, (Saddle Peak, in the Bridger Mountain Range of southwest Montana, USA) captured skiers descending Saddle Peak in ten-second increments. Skier locations were digitized from the photos, then transformed onto a geo-referenced digital elevation model (DEM) such that terrain metrics could be applied to each skier location. Analysis of terrain metrics for each skier point compared slope, profile curvature (downslope), and plan curvature (cross slope) over days with different forecasted avalanche hazard. Terrain metrics on Considerable avalanche hazard days differed significantly from Moderate or Low avalanche hazard days (p-value < 0.001). Transformed data fell within a 49-m horizontal accuracy for all skier point locations with a 95% confidence interval. By capturing all skiers on a slope without their knowledge, the data collected provides a large and diverse data set of the terrain preferences of backcountry skiers under varying conditions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".