The inclusion of ice model uncertainty in 3D Glacial Isostatic Adjustment modelling: a case study from the Russian Arctic
Bibliographic record
Abstract
The western Russian Arctic was partially covered by the Eurasian ice sheet complex during the Last Glacial Maximum (~26 ka BP) and is a focus area for Glacial Isostatic Adjustment (GIA) studies. However, there have been few GIA studies conducted in the Russian Arctic due to the lack of high quality deglacial relative sea-level (RSL) data. Recently, Baranskaya et al. (2018) released a quality-controlled deglacial RSL database for the Russian Arctic that consists of ~400 sea-level index points and ~250 marine and terrestrial limiting data that constrain RSL since 20 ka BP. Here, we use the RSL database to constrain the 3D Earth structure beneath the Russian Arctic, with consideration of the uncertainty in ice model ICE-7G_NA, which is assessed via iteratively refining the ice model with fixed 1D Earth model to achieve a best fit with the RSL data. Also, the uncertainties in 3D Earth parameters and RSL predictions are investigated. We find an optimal 3D Earth model (Vis3D) improves the fit with the deglacial RSL data compared with the VM7 1D model when fixed with the ICE-7G_NA ice model. Similarly, we show improved fit in the White Sea area, where 1D model shows notable misfits, with the refined ice model ICE-7G_WSR when fixed with VM7 Earth model. The comparable fits of ICE-7G_NA (Vis3D) and ICE-7G_WSR (VM7) implies that the uncertainty in the ice model might be improperly mapped into 3D viscosity structure when a fixed ice model is employed. Furthermore, fixed with refined ice model ICE-7G_WSR, we find an optimal 3D Earth model (Vis3D_R), which fits better than ICE-7G_WSR (VM7), and the magnitude of lateral heterogeneity decreases significantly from Vis3D to Vis3D_R. We conclude that uncertainty in the ice model needs to be considered in 3D GIA studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".