Tsunami hazard assessment related to slope failures in coastal waters
Bibliographic record
Abstract
Although subaerial and subaqueous landslides have been responsible for many tsunamis in high-relief coastal areas around the world, routine assessments of these hazards are rarely undertaken. Assessment must draw on the expertise of geoscientists, engineers, and hydrodynamicists, and requires analyses of both the landslide and the resulting waves. Landslide tsunami assessments aim to determine: occurrences of past events likelihood of future occurrences magnitudes of past events locations experiencing greatest impact conditions and triggers that led to failure wave characteristics and coastal run-up. Key assessment considerations include the geologic evidence of past failures, both subaerial and subaqueous, and the written or oral history of past events. These can aid in determining whether further assessment studies are warranted. The general paucity of observations of past events, however, makes empirical assessment difficult. As a consequence, physical and numerical modeling are critical tools in characterizing the phenomena. Because modern numerical models are fast to run and relatively inexpensive, they are now widely used for specific case studies. Much can be learned before failures occur in areas prone to tsunamigenic landslides. Hydrodynamic modeling, combined with geologic and geotechnical evidence, can be used to assess the tsunamigenic potential of landslides. Although definitive estimates of the frequency of occurrence and magnitude of tsunamigenic events are difficult to make, analyses can place valuable constraints on the siting and design of coastal facilities.
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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 | 0.000 | 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) | 0.007 | 0.001 |
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".