Submarine Deposition of a Subaerial Landslide in Taan Fiord, Alaska
Bibliographic record
Abstract
Abstract A large subaerial landslide entered Taan Fiord, Alaska, on 17 October 2015 producing a tsunami with runup to 193 m. We use LiDAR data to show the slide volume to be 76 + 3/−4 million cubic meters and that 51,000,000 m3 entered Taan Fiord. In 2016, we mapped the fjord with multibeam bathymetry and high‐resolution seismic data. Landslide and postlandslide deposits extend 6 km downfjord, are up to 70 ± 11 m thick, and have a total volume of ~147,000,000 m3. Seismic data image a blocky landslide unit and two units deposited immediately after the landslide. The blocky landslide unit is ~65,000,000 m3. We infer it consists dominantly of subaerially derived material and secondarily of fjord floor sediment. The overlying units are likely megaturbidites presumably deposited within minutes to days after the landslide. We infer that these deposits dominantly consist of fjord floor material mobilized and suspended as the slide entered and traveled downfjord. The lower postlandslide unit is up to 35 ± 6 m thick, and the upper unit is up to 12 ± 3 m thick. These deposits are distinctive and will leave a lasting record of the event. This subaerial‐to‐submarine landslide deposit is distinct from other submarine landslide deposits studied in Alaskan fjords because it has a much greater thickness, larger and more angular blocks, distinctive postlandslide megaturbidites, and a higher‐amplitude acoustic signature of the blocky deposit. The tight constraints on the landslide source and deposit volumes, topography, bathymetry, and tsunami runup heights and flow directions should make this a benchmark site for landslide‐tsunami models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".