Solid Earth deformation in Greenland observed by the Greenland’s GNSS Network
Bibliographic record
Abstract
The Greenland ice sheet has lost significant mass over the past two decades. More than 58 permanent Global Navigation Satellite System (GNSS) stations on bedrock, which are part of Greenland’s GNSS Network (GNET), measure the deformation continuously. The solid Earth displacement processes are two-fold: an instantaneous elastic deformation and a slow viscoelastic deformation, which can be attributed to glacial isostatic adjustment (GIA). We have gained new insight into both vertical and horizontal land movement by removing the elastic deformation with high-resolution mass change grids.By including mass change from Greenland and Arctic Canada peripheral glaciers, our estimates for the vertical GNSS velocities align with GIA models, though significant regional discrepancies remain. For the horizontal GNSS velocity component, new Euler poles describing the North American plate where fitted, which is the majority of the horizontal observed GNSS velocity. We compared our inferred horizontal GIA deformation with 26 1D GIA models. We discovered a significant inward contraction field in South Greenland, originating from the Laurentide ice sheet that the GIA models cannot capture. A complete North, East, and Up inferred GIA velocity field for Greenland can be used as a constraint for both GIA models and to target stations with abnormal behaviour where mass change estimates should be improved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".