AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping
Bibliographic record
Abstract
Avalanche risk assessment is complex and challenging, with terrain assessment as one of the most fundamental factors. To aid people's terrain assessment, Parks Canada developed the Avalanche Terrain Exposure Scale (ATES), a system that classifies the severity of avalanche terrain into five classes from non-avalanche terrain to extreme terrain. Manual classification is laborious and dependent on expert's assessments. To ease the process Larsen et al. (2020) developed an automated ATES model (AutoATES v1.0). Although the model allowed large-scale mapping, it had some significant limitations. This paper presents an improved AutoATES v2.0 model improving the potential release area (PRA) model, utilizing the new Flow-Py runout simulation package. Furthermore, it incorporates forest density data in the PRA, in Flow-Py, and in a newly developed post-forest-classification step. AutoATES v2.0 has also been rewritten in open-source software, making it more widely available. The paper includes a validation of the model measured against two consensus maps made by three experts at two different locations in western Canada. For Bow Summit, the F 1 score (a measure of how well the model performs) improved from 64 % to 77 %. For Connaught Creek, the F 1 score improved from 40 % to 71 %. The main challenge limiting large-scale ATES classification is the determination of optimal input parameters for different regions and climates. In areas where AutoATES v2.0 is applied, it can be a valuable tool for avalanche risk assessment and decision-making. Ultimately, our goal is for AutoATES v2.0 to enable efficient, regional-scale, and potentially global ATES mapping in a standardized manner rather than based solely on expert judgment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".