Quantitatively capturing decision-making practices of mechanized ski guides using GPS tracking, avalanche terrain modeling and Bayesian networks
Bibliographic record
Abstract
Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snow-cat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences that can occur due to time pressure and emotional investment. While guiding teams pass on this expertise through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalog and analyze their decision-making process using operational data, GPS tracking, automated avalanche hazard indication mapping, and Bayesian networks. Our initial results use Bayesian networks to model the daily run list decision-making process, where guides code runs as either black (not considered), red (closed), or green (open). To capture the real world decision-making process, we worked with expert guides to build the structure of the decision-making network. Data relevant to run characteristics, current conditions, and prior run list decisions are used to calculate the conditional probability tables of the decision-making model. Our resulting model illustrates the decision-making process and can predict the run list coding with accuracy of roughly 87% compared to 80,854 observed run codes from CMH Galena. These insights provide a baseline for development of future decision-making tools that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".