Effect of Lithospheric Thickness and Radial Mantle Viscosity Profile on Glacial Isostatic Adjustment Crustal Motions in North America
Bibliographic record
Abstract
North America is experiencing vertical and horizontal crustal motion due to glacial isostatic adjustment (GIA). To explore these motions across central and eastern North America, GIA modelling was carried out employing the ICE-6G_C surface loading model. The Earth model response was determined for 500 3-layered mantle viscosity profiles at nine different lithospheric thicknesses, assuming a constrained density and elastic structure. The predictions were compared to observed velocities downloaded from the Nevada Geodetic Laboratory for selected Global Navigation Satellite System (GNSS) sites and were corrected for hydrological loading and current global ice change. The fit is assessed through a Root Mean Square (RMS) calculation of the residual velocities. Scanning across lithospheric thicknesses and viscosity profiles, the preferred models were compiled to assess the overall best fit for vertical, horizontal, and combined crustal motions. The horizontal and combined responses exhibit two optimal viscosity profile ranges dependent on the lithospheric thickness. The viscosity profile for thinner lithospheres (<120 km) is akin to other published profiles but inferred mantle viscosities shift by an order of magnitude at thicker lithospheres (≥120 km). The optimal viscosity for the vertical velocities is similar to published profiles with a preferred lithosphere thickness of 100 km. Tests with a different loading model (ICE-7G) and without hydrological corrections give similar results. Despite the exhaustive exploration of a constrained parameter space, the significant remaining horizontal residuals (RMS of the preferred model is 0.56 mm/yr, RMS of horizontal observations is 1.24 mm/yr) suggests the need for more complex Earth models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".