Validating the Space-Time Model for Infrequent Snow Avalanche Events Using Field Observations from the Columbia and Rocky Mountains, Canada
Bibliographic record
Abstract
Avalanche runout distance and return period estimates are essential to snow avalanche risk assessment for hazard mapping and mitigation design. We present a validation of the Space-Time (ST) model, a statistical model that expresses runout distance as a function of return period. The validation is based on field observations of tree damage and tree ring data of infrequent (1:5 to 1:100 year return period) avalanches from 34 paths in the Canadian Rocky and Columbia Mountains. While the ST model has been applied successfully for a longer return period (>100 year) runouts in one path, our validation showed that it does not independently estimate infrequent runout distances with sufficient accuracy for hazard mapping and mitigation design. As with estimating extreme runouts, it should be used in combination with other methods. The model was found to perform differently across mountain ranges, and tended to estimate runout distance downslope of those observed in the field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".