Emplacement dynamics of supraglacial rock avalanches: details from the 2016 Lamplugh event, Alaska
Bibliographic record
Abstract
In Glacier Bay Park and Preserve, Alaska, at least 25 rock avalanches occurred since the mid-1980s. The 2016 Lamplugh rock avalanche, with roughly 70 Mm3 deposit volume, is one of the larger events within the park. It originated from a north-facing bedrock ridge without any obvious trigger, and spread 10 km down Lamplugh Glacier. Based on field surveys, high-resolution digital elevation models, and continuous seismic data, we show that the emplacement dynamics of this supraglacial rock avalanche can be described by two distinct stages. Clear long-period seismic signals during Stage-1 record strong interactions of the rock avalanche debris with the ground, suggesting dynamic processes such as grain collisions and fragmentation ('active or dynamic emplacement' of a granular flow). During this first stage, the debris traveled about 5 km from the base of the slope; its deposit is thin and stretched with a dominant dry and flat area in the center, and has narrow raised margins. Stage-2 was essentially aseismic at long periods and dominated by low-friction sliding at slow deceleration rates ('passive sliding'). This sliding produced the distal roughly third of the total runout length where the deposit has a higher density of flowbands and more prominent, raised margins from entrainment and bulldozing of snow. The higher apparent mobility of supraglacial landslides (relative to their counterparts in other runout environments) may be explained by this two-stage model.
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 | 0.000 |
| 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 | 0.000 | 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".