North American Crustal Motion and Glacial Isostatic Adjustment Model Predictions
Bibliographic record
Abstract
<p>A suite of forward GIA model predictions, spanning a wide range of layered mantle viscosity and lithospheric thickness values, is compared to observed horizontal crustal motions in North America to discern optimal model parameters in order to minimize a root-mean-square (RMS) measure of the velocity residuals. To obtain the Earth model response, a combination of the full normal mode analysis and the collocation method is implemented. It provides a means to determine the surface loading response automatically and robustly to 1-dimensional (radially varying) Earth models, while retaining as much of the physics of the normal mode method as numerically feasible, given documented issues with singularities along the negative inverse-time axis in the Laplace transform domain. This method enables the exploration across a wide parameter range (for the lower mantle, transition zone, asthenosphere, and thickness of the elastic lithosphere) to find optimal combinations to explain horizontal crustal motion in North America. The analysis utilizes crustal motion rates from approximately 300 GNSS sites in central North America (Canada and United States) provided by the Nevada Geodetic Laboratory.  Preliminary results indicate that horizontal crustal motion predictions generated with a thin lithosphere, 40 – 60 km, produce horizontal motions that are strongly discrepant with the observations and have velocity residuals larger than the null model (modelled horizontal motion set to zero). As the lithospheric thickness increases, from 80 km to 240 km, the horizontal motion residuals gradually decrease with no minimum apparent for the thicknesses thus far considered. The residual velocities for the best-fitting models appear to carry a remaining signal, confirming previous inferences of limitations to spherically symmetric Earth models in modeling horizontal crustal motions in North America.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".