Weakened Lithosphere Beneath Greenland Inferred From Effective Elastic Thickness: A Hot Spot Effect?
Bibliographic record
Abstract
Abstract The effective elastic thickness ( T e ) of the lithosphere provides geophysical information about long‐term flexural strength and can be used to constrain thermorheological properties of the lithosphere. T e is typically calculated from the spectral analysis of gravity and topography data; variations in T e are, however, not well resolved in Greenland due to poor constraints on crustal structure (including crustal thickness) and complications due to ice loading. In addition, geological and geophysical constraints on the tectonic history of Greenland are sparse due to the thick ice cover. Here we use the global gravity model EIGEN‐6C4 together with a new model of the crust‐mantle boundary to obtain a high‐resolution T e map of Greenland. The distribution of T e indicates reduced strength in the lower crust and lithospheric mantle beneath southern and central Greenland, which may be due to the passage of the Iceland hot spot during the last 100 Ma. In contrast, the northern part of Greenland shows a large T e , implying mechanical coupling between crust and uppermost mantle and suggesting the existence of a cold and strong tectonic unit. In a relative sense, the distribution of T e values is consistent with estimates of lithospheric thickness based on seismic velocity models, indicating a dominantly thermal control on lithospheric structure and evolution.
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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 | 0.000 | 0.000 |
| 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".