Upper mantle temperatures illuminate the Iceland hotspot track and understanding of ice–Earth interactions in Greenland
Bibliographic record
Abstract
The thermal structure of the Earth beneath Greenland reflects the tectonic history of the region and impacts ice sheet evolution due to surface heat flow and the influence of temperature on Earth rheology, and thus glacial isostatic adjustment. We present results from a probabilistic joint inversion of multiple satellite and land-based datasets to determine the thermal structure of the lithosphere and upper mantle beneath Greenland and consider the implications for our understanding of the tectonic history, isostatic deformation, and Greenland ice sheet evolution. Passage of Greenland over the Iceland hotspot is well known but there remains considerable debate on the trajectory of this path. Our findings reveal strong lateral variability in thermal structure that is consistent with reconstructions of a west-to-east hotspot track across central Greenland. Applying our temperature model to infer mechanical properties of the solid Earth reveals viscosity variations reaching 3 orders of magnitude in the upper mantle. We generate an ensemble of plausible 3D viscosity models and produce quality fits to both paleo sea level and contemporary vertical land motion datasets. This result supports the veracity of our temperature model and questions the need for a large component of transient deformation to explain the observations. Our regional temperature and viscosity models can be used to develop improved reconstructions and understanding of past Greenland ice sheet changes and explore the influence of 3D Earth structure on simulating ice sheet and sea level evolution in the past and future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".