Early Holocene Greenland-ice mass loss likely triggered earthquakes and tsunami
Bibliographic record
Abstract
Due to their large mass, ice sheets induce significant stresses in the Earth's crust. Stress release during deglaciation can trigger large-magnitude earthquakes, as indicated by surface faults in northern Europe. Although glacially-induced stresses have been analyzed in northern Europe, they have not yet been analyzed for Greenland. We know that the Greenland Ice Sheet experienced a large melting period in the early Holocene, and so here, we analyze glacially-induced stresses during deglaciation for Greenland for the first time. Instability occurs in southern Greenland, where we use a combined analysis of past sea level indicators and a model of glacially-induced fault reactivation to show that deglaciation of the Greenland Ice Sheet may have caused a large magnitude earthquake or a series of smaller magnitude earthquakes around 10,600 years ago offshore south-western Greenland. The earthquake(s) may have shifted relative sea level observations by several meters. If the earthquake-induced stress release was created during a single event, it could have produced a tsunami in the North Atlantic Ocean with runup heights of up to 7.2 m in the British Isles and up to 7.8 m along Canadian coasts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".