Dynamic analysis of an extraordinarily mobile rock avalanche in the Northwest Territories, Canada
Bibliographic record
Abstract
The pre-historic rock avalanche at Avalanche Lake was a spectacularly mobile rock avalanche that resulted in the largest documented runup of any landslide on earth. The runout of the 200 Mm3 event was a complex and three-dimensional process that created three distinct depositional lobes. There is some controversy as to whether the presence of glacial ice played an important role in the dynamics of this event. To investigate this hypothesis an advanced, three-dimensional numerical landslide runout model was used to reconstruct the dynamics of this event. It was found that a conventional runout model is able to reproduce the bulk characteristics of this event, including its spectacular runup, without accounting for glacial ice. A sensitivity analysis was performed to determine the factors that control the mobility of this event. It was found that low strength in the source zone, as well as the presence of significant internal strength, is required to reproduce the 600 m runup. This has important implications for the hazard analysis of rock avalanches. It appears as though large-volume rock avalanches can move with a friction angle lower than that expected for dry fragmented rock, and the runout process can be strongly influenced by internal strength. These important factors must be accounted for when performing forward analyses of this type of natural disaster.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".