Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery
Bibliographic record
Abstract
In the USA, climate change is expected to have an adverse impact on slope stability in Alaska. However, to date, there has been limited work done in Alaska to assess if changes in slope stability are occurring. To address this issue, we used 30-m Landsat imagery acquired from 1984 to 2016 to establish an inventory of 24 rock avalanches in a 5000-km2 area of Glacier Bay National Park and Preserve in southeast Alaska. A search of available earthquake catalogs revealed that none of the avalanches were triggered by earthquakes. Analyses of rock-avalanche magnitude, mobility, and frequency reveal a cluster of large (areas ranging from 5.5 to 22.2 km2), highly mobile (height/length < 0.3) rock avalanches that occurred from June 2012 through June 2016 (near the end of the 33-year period of record). These rock avalanches began about 2 years after the long-term trend in mean annual maximum air temperature may have exceeded 0 °C. Possibly more important, most of these rock avalanches occurred during a multiple-year period of record-breaking warm winter and spring air temperatures. These observations suggested to us that rock avalanches in the study area may be becoming larger because of rock-permafrost degradation. However, other factors, such as accumulating elastic strain, glacial thinning, and increased precipitation, may also play an important role in preconditioning slopes for failure during periods of warm temperatures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".