Vertical Displacements and Sea‐Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: An Ensemble Modeling Approach
Bibliographic record
Abstract
Abstract Glacial isostatic adjustment (GIA) describes the response of the solid Earth, oceans, and gravitational field to the spatio‐temporal evolution of ice sheets during a glacial cycle. Present‐day vertical displacements and sea‐level changes vary throughout eastern North America in response to the melting of the Laurentide Ice Sheet following the Last Glacial Maximum. We use the open‐source software SELEN4.0 (a SealEveL EquatioN solver) to investigate the influence of GIA on vertical land motions and sea‐level changes in eastern North America. Further, we evaluate the uncertainties associated with the lithospheric thickness and viscosity structure using an ensemble modeling approach (129,956 total simulations). We identify the best‐fitting rheological profiles by comparing modeled vertical displacements to vertical velocity rates derived from Global Positioning System (GPS). We find a general pattern of subsidence (causing accelerated relative sea‐level rise) in the eastern United States region and uplift (causing relative sea‐level fall) in the eastern Canada region consistent with previous studies for two tested ice sheet models (ICE‐6G(VM5a) and ICE‐7G(VM7)). Overall, we find lower rates of modeled vertical displacement using ICE‐6G(VM5a) compared with ICE‐7G(VM7), which produces lower residuals when compared with the GPS‐derived vertical velocity rates. Our ensemble analysis identifies adjustments to the nominal VM5a and VM7 viscosity models that improve fits to the GPS‐imaged vertical velocity rates throughout eastern North America and on the North American Atlantic Coast. The differences in our best‐fitting models for inland versus coastal regions highlight the importance of exploring lateral viscosity variations for GIA modeling throughout North America and elsewhere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".