Earthquakes induced by ice-mass loss: A case example for southern Greenland
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. Thus, the current ice-mass loss in Greenland can be accompanied by earthquakes. Here, we will present an example of a possible large magnitude earthquake that occurred during the large melting period of the Greenland Ice Sheet in the early Holocene. The glacially induced stresses showed an instability occurring at 10,600 years ago. An offset in past sea level indicators falls within the same time frame, which gave us indications that the stresses have been released by an earthquake. The potential fault could have slipped up to 47 m, resulting in a large magnitude earthquake, if only one event occurred. The earthquake may have shifted relative sea level observations by several meters. In addition, as the potential fault is located offshore, the earthquake 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. Thus, ice-mass loss is strongly linked to the occurrence of earthquakes and even earthquakes-related tsunami. These scenarios due to a changing cryosphere can have effects for all countries bordering the North Atlantic Ocean and are in addition to the well-known sea-level rise.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".