Sea level curves, geoid rate and uplift rate from composite rheology in glacial isostatic adjustment modeling
Bibliographic record
Abstract
Laboratory experiments show that both diffusion creep and power-law creep can exist for realistic mantle conditions. Therefore a composite rheology which includes both creep laws might be a better approximation of the deformation process in the mantle. Here we study the effect of such a rheology on glacial isostatic adjustment (GIA) observables. Composite rheology has in the past been shown to provide a better fit to sea level data for a wide range of parameters investigated with 2D finite element models. Here, we use the Coupled Laplace Finite Element Method for an incompressible 3D spherical self-gravitating Earth to study the effect of composite rheology on relative sea level (RSL) curves, maximum present-day uplift rate and maximum present-day geoid rate with the ICE-5G model. The long computation time of this model limits the number of cases that can be investigated to a handful. The stress exponent is taken to be 3, the pre-stress exponent (A) derived from a uni-axial stress experiment is varied between 3.3 x 10−33/10−34/10−35/10−36 Pa−3s−1, and the Newtonian viscosity η is varied between 1/3/9 x 10 Pas. Because the ice models are developed under the assumption of linear rheology any rheology with a non-linear component usually under predicts the uplift rate and geoid rate. Therefore, to see if the ice model can provide a better fit, we investigate simple modifications to the ICE-4G model, such as i) scaling of the ice height by 1.5 and 2.0; and ii) delay in glaciation by 1 and 2 kyears.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".