Tsunami Runup Survey Data From The Taan Fjord Landslide Event
Bibliographic record
Abstract
On 17 October 2015, a mountainside collapse at the terminus of Tyndall Glacier in southeast Alaska generated a landslide-triggered mega-tsunami. The landslide sent approximately 7.6 × 10 7 m 3 of rock and glacial sediment into Taan Fjord, abruptly displacing the water. The ensuing tsunami reached a peak runup of ~193 m on a steep slope directly across from the landslide, ranking among the highest tsunami runups ever documented. The wave inundated over 20 km² of terrain around Taan Fjord and Icy Bay, stripping forests and depositing sediment up to hundreds of meters inland. In 2016, a comprehensive field campaign surveyed the event, recording precise tsunami runup elevations, flow depths, and inundation distances at dozens of sites throughout the fjord and adjacent coastlines. Here we present the resulting datasets: a georeferenced catalogue of tsunami runup measurements, high-resolution topography and bathymetry data, and an extensive collection of field photographs. These data provide a quantitative record of a mega-tsunami’s onshore effects, intended as a benchmark dataset for landslide-tsunami modeling and hazard assessment.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".