The impact of regional-scale variability in upper mantle viscosity on GIA in West Antarctica
Bibliographic record
Abstract
West Antarctica is underlain by a laterally heterogenous upper mantle, with localized regions of mantle viscosity reaching several orders of magnitude below the global average. Accounting for 3-D variability in upper mantle structure in glacial isostatic adjustment (GIA) simulations has been shown to significantly impact the predicted spatial rates and patterns of crustal deformation, geoid and sea-level changes. Uncertainty in constraining the viscoelastic structure of the solid Earth remains a major limitation in GIA modeling. To date, investigations of the impact of 3-D Earth structure on GIA have adopted solid Earth viscoelastic models based on global- and continental-scale seismic imaging with variability at spatial scales >150 km. However, regional body-wave tomography shows mantle structure variability at smaller spatial scales (~50-100 km) in central West Antarctica (Lucas et al., 2020). Here, we investigate the effects of incorporating this smaller-scale lateral variability in upper mantle viscosity into 3-D GIA simulations. Lateral variability in upper mantle structure at the glacial basin scale is found to have a significant impact on GIA model predictions, especially in coastal regions undergoing rapid ice mass loss. For example, incorporating a transition from lower viscosity at the mouth of Thwaites Glacier to higher viscosity further upstream impacts the predicted rate and pattern of solid Earth deformation and sea-level change in response to ongoing and projected ice mass loss, with possible implications for the evolution of the overlying ice and the interpretation of geophysical observables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".