Vertical deformation in Greenland: separation of past and present-day ice mass loss contributions
Bibliographic record
Abstract
We compute daily GPS solutions for about 200 permanent stations in Greenland, Scandinavia and Canada for the 2000 – 2023 period, using the CNES/GINS software in precise point positioning with integer ambiguity resolution (IPPP) mode. The observed vertical displacements are caused by both past- and present-day ice mass (PDIM) changes. The glacial isostatic adjustement (GIA) is the visco-elastic Earth’s response to the Pleistocene glaciation and deglaciation, whereas the PDIM is often estimated assuming an elastic Earth’s response.We revisit the problem of the separation of GIA and PDIM using state-of-the-art ice models (for example, ICE-6G and ICE-7G) and observations from space gravimetry (GRACE and GRACE Follow On) and altimetry (CryoSat-2 and ICESat-2).In particular, we investigate different rheology models, including the classical Maxwell model used in GIA modeling, but also the Burgers model allowing transient anelastic deformation at timescales of 10 to 20 years.We found that the Burgers model with a transient viscosity of about 1018 Pa.s in the upper mantle, combined with the VM5a or VM7 viscosity profiles (Maxwell component) is in better agreement with the observed GPS vertical displacements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".