Lithosphere thickness and mantle viscosity inverted from GPS-derived deformation rates in Fennoscandia
Bibliographic record
Abstract
Crustal deformation in Fennoscandia is associated with the glacial isostatic adjustment (GIA) process that is caused by ongoing stress release of the mantle after removal of the Late Pleistocene ice sheet by ∼10 cal ka BP. With an earth model of defined structure and rheology and an ice-sheet model of known melting history, the GIA process can be simulated by geophysical models, and the surface deformation rates can be calculated and used to compare with global positioning system (GPS) observations. Therefore, the crustal deformation rates observed by GPS in Fennoscandia provide constraints on the geophysical models. On the basis of two ice sheet models (ANU-ICE and ICE-5G) reconstructed independently by the Australian National University (ANU) and University of Toronto, we use the GPS-derived deformation rates to invert for lithosphere thickness and mantle viscosity in Fennoscandia. The results show that only a three-layer earth model can be resolved from current GPS data, providing robust estimates of effective lithosphere thickness, upper and lower mantle viscosity. The earth models estimated from inversion of GPS data with two different ice sheet models define a narrow range of parameter space: the lithosphere thickness between 93 and 110 km, upper mantle viscosity between 3.4 and 5.0 × 1020 Pa s, and lower mantle viscosity between 7 × 1021 and 13 × 1021 Pa s. The estimates are consistent with those inverted from relative sea-level indicators.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".