Wave Characteristics of Tsunamis Generated by Landslides of Varying Size and Mobility
Bibliographic record
Abstract
Landslide generated tsunamis are a natural phenomenon, capable of causing catastrophic consequences at the site of impact as well as some kilometers away. In order to increase understanding of these hazards, a series of laboratory experiments were conducted in a large-scale flume investigate the wave characteristics in the near-field and far-field based on a variety of landslides. The first objective is to examine the difference between the near-field maximum wave amplitude generated by highly mobile (water) and dry granular landslides. The results indicate that quantifying time and length scales are imperative in order to accurately estimate the maximum amplitude for highly mobile flows. The relationship between the time and length scales has a direct influence on the wave amplitude. The dry granular landslide decelerates much faster and therefore produce a smaller near-field maximum wave amplitude than those produced by highly mobile flows. The second objective is to investigate the relationship between near-field wave shape of an impulse generated wave and the landslide properties. Quantifying the wave shape in terms of asymmetry allowed for the development of a novel framework, which provides a time-series for an impulse wave generated by a landslide with certain characteristics of thickness and velocity. The third objective explores the near-field maximum wave amplitude generated by a higher mobility granular flow. The impulse waves generated in this study were in between the amplitude of the dry granular and highly mobile flows previously tested. This indicates that mobility can be quantified using the relationship between the time and length scales, and impulse waves generated by the highly mobile (water) flows is an accurate upper-bound of mobility.
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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 | 0.000 | 0.000 |
| 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".