The lateral heterogeneity of Glacial Isostatic Adjustment modelling across the Arctic
Bibliographic record
Abstract
The Arctic has been key area for glacial isostatic adjustment (GIA) studies because it was covered by large ice sheets at the Last Glacial Maximum. Previous GIA studies applied mainly 1D Earth models. The few studies that did include 3D Earth structures have not considered the lateral heterogeneity differences across different regions of the Arctic. Here, using the latest standardized deglacial relative sea-level (RSL) databases from Norway and Russian Arctic, we investigate the effects of 3D structure on GIA predictions and explore the magnitudes of the lateral heterogeneity in both regions.The 3D Earth structure consists of 1D background viscosity model (ηo) and lateral viscosity variation, the latter is derived from the shear velocity anomaly from seismic tomography model and controlled by scaling factor (ß) denoting the magnitude of lateral heterogeneity.The Norway RSL database includes 413 sea-level index points (SLIPs), 175 marine limiting data and 433 terrestrial limiting data, while the Russian Arctic database includes 353 SLIPs, 78 marine limiting data and 92 terrestrial limiting data.We find 3D Earth structures have significant influences on RSL predictions and the optimal 3D model notably improves the fit with RSL data. However, we realize RSL data from Norway and Russian Arctic prefer different 3D structures to provide the best fits. The Russian Arctic database prefers a softer background viscosity model (ηo), but larger scaling factors (ß) than those preferred by Norway database. We further test the extent to which the 3D structure can be eliminated by refinement of ice model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".