How Alaska's Barry Arm Can Help Us Prepare for Climate Change Hazards
Bibliographic record
Abstract
A slope at Barry Arm, in Alaska’s Prince William Sound, is deforming at a varying rate up to tens of meters per year above a retreating glacier and deep fjord that is a popular recreational destination. If the estimated 500 million cubic meters of unstable material on this slope were to fail catastrophically, the impact of the landslide with the ocean would produce a tsunami that would not only endanger those in its immediate vicinity, but likely also those in more distant areas such as the port of Whittier, 50 km away. The discovery of this threat was happenstance, and the response so far has been cobbled together from over a dozen existing grants and programs. Remotely sensed imagery could have revealed this hazard a decade ago, but nobody was looking, highlighting our lack of coordination and preparedness for this growing hazard driven by climate change. As glaciers retreat, they can simultaneously destabilize mountain slopes and expose deep waters below, creating the potential for destructive tsunamis. The settings where this risk might occur are easily identified, but more difficult to assess and monitor. Unlike for volcanoes, active faults, landslides, and tectonic tsunamis, the US has conducted no systematic assessment of tsunamis generated by subaerial landslides, nor has the US established methods for monitoring or issuing warnings for such tsunamis. The U.S. National Tsunami Warning Center relies on seismic signals and sea-level measurements to issue warnings; however, landslides are more difficult to detect than earthquakes, and the resultant tsunamis often would reach vulnerable populations and infrastructure before water level gages could help estimate the magnitude of the tsunami. Also, integrating precursory motion and other clues of an impending slope failure into a tsunami warning system has only been done outside the US (e.g Norway: Blikra et al., 2012). Barry Arm is a dramatic case study highlighting these challenges and may provide a model for mitigating the threat of tsunamis generated by subaerial landslides enabled by glacial retreat elsewhere.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".