Snow avalanches in western Canada: investigating change in occurrence rates and implications for risk assessment and mitigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Snow avalanche frequency and magnitude, required for risk analysis, are best determined using historical occurrence records. In Canada, reliable records are sparse and relatively short term, extending back 30–50 years. Some Canadian avalanche forecasters suspect that climate change has increased avalanche activity in recent years. Should an increasing trend exist, analyses based on historical occurrence rates would underestimate future risk to people and infrastructure in mountain regions. We analyse 30 years of occurrence records to investigate whether a trend exists, and assess its strength. A Bayesian hierarchical model is used to estimate occurrence rate trends across six geographical zones in western Canada. The results suggest that natural avalanche occurrence rates have decreased or stayed constant; however, there is a very high level of uncertainty. This uncertainty will need to be factored into decision-making processes. To assist in this, we discuss the effect of long-term changes in avalanche occurrence rates in terms of consequences and vulnerability. We also recommend strategies for improving mitigation practices so that avalanche control operations can better adapt to changing and less predictable environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it