A method of deriving operation-specific ski run classes for avalanche risk management decisions in mechanized skiing
Bibliographic record
Abstract
An in-depth understanding of the nature of the available terrain and its exposure to avalanche hazard is crucial for making informed risk management decisions when travelling in the backcountry. While the Avalanche Terrain Exposure Scale (ATES) is broadly used for providing recreationists with terrain information, this type of terrain classification has so far only seen limited adoption within the professional ski guiding community. We hypothesize that it is the generic nature and small number of terrain classes of ATES and its precursor systems that prevent them from offering meaningful assistance to professional decision makers. Working with two mechanized skiing operations in British Columbia, Canada, we present a new approach for deriving terrain classifications from daily terrain assessment records. We used a combination of self-organizing maps and hierarchical clustering to identify groups of ski runs that have been assessed similarly in the past and organized them into operation-specific ski run hierarchies. We then examined the nature of the emerging ski run hierarchies using comprehensive run characterizations from experienced guides. Our approach produces high-resolution ski run hierarchies that offer a more nuanced and meaningful perspective on the available skiing terrain and provide new opportunities for examining professional avalanche risk management practices and developing meaningful decision aids.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".