Bibliographic record
Abstract
ABSTRACT: Deep slab avalanches release on persistent weak layers of facets, surface hoar, depth hoar, or poorly bonded crusts and are generally hard to forecast. They are triggered either naturally from weather or are artificially triggered from localized dynamic loads such as skiers, snowmobilers, and explosives. For natural deep slab avalanches, weather preceding the release plays a key role in formation. For deep slab avalanches that are artificially triggered, preceding weather can also have a strong role. For this research, 51 deep slab avalanches were accessed in western Canada between 1993 and 2013 to obtain information on the persistent weak layer and overlying slab. Weather parameters such as precipitation amount, daily minimum and maximum temperature, and wind speed and direction were obtained from the nearest weather station for the two weeks prior to release of the accessed avalanches. Results indicate that the accessed natural deep slab avalanches typically occurred from either rapid mass loading via precipitation or wind transported snow or from snowpack warming by air temperature or incoming short wave radiation. Higher cumulative precipitation and wind loading potential amounts were observed for the avalanches that likely released from rapid mass loading. The natural releases that likely occurred from solar warming did not have high amounts of precipitation, wind loading, or warming and experienced clear skies during the day of release. The most amount of warming was observed for the avalanche that likely released from temperature warming. Similar weather trends for both natural and artificially triggered avalanches indicate the importance of analyzing the snowpack along with preceding weather.
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.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".