Dropstones in Lacustrine Sediments as a Record of Snow Avalanches—A Validation of the Proxy by Combining Satellite Imagery and Varve Chronology at Kenai Lake (South-Central Alaska)
Bibliographic record
Abstract
Snow avalanches cause many fatalities every year and damage local economies worldwide. The present-day climate change affects the snowpack and, thus, the properties and frequency of snow avalanches. Reconstructing snow avalanche records can help us understand past variations in avalanche frequency and their relationship to climate change. Previous avalanche records have primarily been reconstructed using dendrochronology. Here, we investigate the potential of lake sediments to record snow avalanches by studying 27 < 30-cm-long sediment cores from Kenai Lake, south-central Alaska. We use X-ray computed tomography (CT) to image post-1964 varves and to identify dropstones. We use two newly identified cryptotephras to update the existing varve chronology. Satellite imagery is used to understand the redistribution of sediments by ice floes over the lake, which helps to explain why some avalanches are not recorded. Finally, we compare the dropstone record with climate data to show that snow avalanche activity is related to high amounts of snowfall in periods of relatively warm or variable temperature conditions. We show, for the first time, a direct link between historical snow avalanches and dropstones preserved in lake sediments. Although the lacustrine varve record does not allow for the development of a complete annual reconstruction of the snow avalanche history in the Kenai Lake valley, our results suggest that it can be used for long-term decadal reconstructions of the snow-avalanche history, ideally in combination with similar records from lakes elsewhere in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".