Improved Greenland glacial isostatic adjustment models with 3D Earth structure inferred from the joint inversion of regional data sets
Bibliographic record
Abstract
<!--!introduction!--> Changes in sea level and vertical land motion associated with glacial isostatic adjustment (GIA) are embedded in paleo and geodetic data sets used to constrain the past and future evolution of the Greenland ice sheet. Thus, understanding of ice sheet evolution goes hand in hand with our ability to simulate the GIA signal accurately. We aim to improve the accuracy of Greenland GIA simulations by interrogating regional geophysical data sets to determine better 3-D models of Earth structure in this region. We use a self-consistent Bayesian joint inversion framework (LitMod) to constrain lithosphere and shallow mantle properties and their uncertainty from multiple data sets. The inversion results indicate a high sensitivity to the input seismic dataset, so we incorporate a new, regional high-resolution surface wave dataset based on the two-station interferometry method. In terms of simulating GIA, a key inversion output is the regional temperature field. We sub-sample a high variance set of 25 temperature fields to define 25 models of the lithospheric thickness (LT) and 50 models of sub-lithosphere viscosity structure (using two different scalings). Our results indicate that viscosity and LT vary, respectively, by 3-4 and 2 orders of magnitude across Greenland. So the predicted GIA signal shows significant differences compared to simulations based on the more traditional 1-D (spherically symmetric) viscosity models. We will present results based on these 50 Earth models and two different ice history models and compare them to geological reconstructions of relative sea-level change and GNSS observations of vertical land motion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".