Space-geodetic and water level gauge constraints on continental uplift and tilting over North America: regional convergence of the ICE-6G_C (VM5a/VM6) models
Bibliographic record
Abstract
We present a series of analyses of the regional convergence of the iterative methodology that has been developed for use in the construction of global models of the glacial isostatic adjustment process. Our specific focus is upon the North American component of such models which embodied the largest concentration of grounded land ice at the Last Glacial Maximum. We show that, although the introduction of the VM6 viscosity structure helps the global ICE-6G_C (VM5a) model to improve the fit to relative sea level data from the region of forebulge collapse along the U.S. East coast, it also leads to a significant misfit to the totality of the available space-geodetic observations, which the original ICE-6G_C (VM5a) model was able to fit with high accuracy. This raises the issue of the convergence of the iterative methodology being employed in the process of model construction. We demonstrate through detailed further analysis that a fully converged solution which reconciles all available data from the continent, including additional data on the time dependent de-levelling of the Great Lakes region, is obtained through modest further modifications of both the viscosity structure of the model and the North American component of the surface mass loading history.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".