GEOMORPHIC IMPACTS CAUSED BY HISTORIC LANDSLIDE TSUNAMIS IN ALASKA, U.S.A., BRITISH COLUMBIA, CANADA, AND WESTERN GREENLAND
Bibliographic record
Abstract
In the last century some of the largest historic landslide tsunamis on record have occurred in some of the most remote regions of the world. The remoteness of these kinds of sites has led to a limited understanding of the geomorphic impacts caused by historic landslide tsunamis, stemming from inherent difficulties with conducting field investigations in remote places. In addition to field investigative work, remote sensing techniques have proven to be useful for assessing changes to vegetation and topography caused by landslide tsunamis. The degree to which geomorphic impacts caused by landslide tsunamis are preserved in satellite images over time has not been studied extensively though. To improve our understanding of geomorphic changes caused by historic landslide tsunamis, we used satellite images to develop normalized difference vegetation index images and differenced normalized difference vegetation index images, with the purpose of quantifying vegetation loss and vegetation recovery following historic landslide tsunami events, and for estimating local wave runup. We analyzed geomorphic impacts caused by four historic landslide tsunamis: 1) the 21 November 2000 Paatuut, Greenland event, 2) the 4 December 2007 Chehalis Lake, British Columbia, Canada event, 3) the 17 October 2015 Taan Fjord, Alaska event, and 4) the 17 June 2017 Karrat Fjord, Greenland event. Our results reveal how vegetation indices can be used to quantify geomorphic impacts and vegetation loss from landslide tsunamis and speed of vegetation recovery. We found vegetation recovery to be relatively slow at the four sites, indicating geomorphic impacts of landslide tsunamis can remain relatively well-preserved for more than a decade, but preservation is highly dependent on the overall erosional intensity of the local environment. Further analysis of older historic landslide tsunami events is needed to determine the duration required for impacts to become completely muted in the landscape.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".