Using time lapse photography to document terrain preferences of backcountry skiers
Bibliographic record
Abstract
Travel in avalanche terrain requires considered and careful selection of appropriate terrain to reduce exposure to avalanche danger. In many parts of the world, recreational backcountry skiers in avalanche terrain are aided by a regional avalanche forecast. The overall aim of an avalanche forecast is for users to adjust their terrain choices in response to the avalanche danger rating and avalanche problem, thereby reducing their risk of an avalanche involvement. In this paper we present a novel passive observation technique to assess how lift assisted backcountry skiers adjust their terrain use in response to the avalanche danger rating. This paper develops and demonstrates a method to record the terrain metrics of all skiers on an avalanche-prone backcountry slope. Using a remote time-lapse camera focused on a high skier-use backcountry slope, we anonymously recorded the descent route of skiers in ten-second increments. Using 31,966 images over 13 days and 7499 skier point locations, skier locations were digitized from the images, then transformed onto a geo-referenced digital elevation model (DEM) such that terrain metrics could be extracted for each anonymous skier location. When these location points are compared to simultaneous GPS measurements, the horizontal accuracy was estimated to be within a 49-m horizontal accuracy, with a 95% confidence interval. Analysis of the terrain metrics for each skier point compared slope, profile curvature (downslope), and plan curvature (cross slope) over days with different forecasted avalanche danger ratings. This statistical analysis was qualitatively supported by a review of the spatial patterns of the terrain choices on these days. Furthermore, we used this technique to estimate group size, and found a surprising number of solo skiers, even on Considerable avalanche danger days. By remotely photographing all skiers on a slope, the data collected provides a large and diverse data set of the terrain preferences of backcountry skiers under varying avalanche conditions, with limited bias. These results have implications for avalanche education by enhancing our understanding of specific terrain management skills by backcountry skiers.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".