Compressible vs. incompressible in glacial isostatic adjustment models: Does it matter?
Bibliographic record
Abstract
Glacial isostatic adjustment (GIA) models provide estimates of velocity, gravity, stress, and sea-level change based on ice-loading scenarios from past glaciations. These models require extensive input, including ice histories and a variety of Earth model parameters that describe the 3D structure and rheology. Different assumptions can be made regarding material parameters, particularly in terms of compressibility, which is described by the Poisson’s ratio. Incompressible materials (Poisson’s ratio equal to 0.5) do not change volume under deformation. However, seismological observations indicate that the Poisson’s ratio in the lithosphere and mantle deviates from 0.5, typically being much smaller, which reflects the presence of compressible materials. Consequently, GIA models must account for compressibility in their material parameters as well as in the solved equations. Despite this, some GIA model codes consider only incompressible materials.Here, we will show the effect of compressible versus incompressible Earth models on changes in sea level, velocity, gravity, and stress using a newly developed compressible finite-element code. The new GIA model code incorporates the sea-level equation with moving coastlines and rotational feedback, accounts for both grounded and floating ice, removes rigid-body rotation, and calculates deformation in the centre-of-mass frame. Importantly, this global-scale analysis, using the new code, is the first to explore how glacially induced stresses obtained from a spherical GIA model are affected by assumptions about compressibility.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".