Rapid delta growth and tsunami disturbance in Lituya Bay, Alaska
Bibliographic record
Abstract
Tidewater glaciers in Alaska have some of the highest reported erosion rates globally, transporting large volumes of sediment that fill fjords and build deltas. In Lituya Bay, southeastern Alaska, two ice-contact deltas below the former Lituya and North Crillon tidewater glaciers have grown rapidly since the mid-20th century adjacent to the active plate-boundary Fairweather fault. We have explored the drivers of sudden delta growth in the context of seismic activity, tsunamis, and glacier and catchment dynamics. Based on repeated bathymetric surveys, we found that mean sedimentation rates declined in northern Lituya Bay in the past ~100 years from 9.9±0.3 × 10 6 m 3 yr -1 (1926- 1959) to 7.7±0.2 × 10 6 m 3 yr -1 (1959-2023). Tsunamis disrupted delta growth until the mid-20th century, one following a M7.8 earthquake on Fairweather fault, eroding and redistributing parts of the Lituya delta in 1958. We surmise that the absence of major tsunami or seismic events since 1958 enabled the combined area of Lituya and Crillon deltas to grow ~4 km 2 between 1985 and 2025. Since 1959, the Lituya delta has trapped twice as much sediment as the Crillon delta. We attribute the faster growth of the Lituya delta to a larger hydrologic catchment, a higher meltwater supply from rapidly thinning Lituya Glacier, and glacial lake outburst floods (GLOFs) from ice-dammed Desolation Lake, which have flooded the Lituya delta almost annually. Investigating the rates and controls of delta growth is essential for assessing the hazard of delta-front failures and fjord tsunamis in rapidly changing deglaciating coastal regions.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".