Prediction of decadal slope changes in Canada by glacial isostatic adjustment modellingThis article is one of a series of papers published in this Special Issue on the theme <i>GEODESY</i>.
Bibliographic record
Abstract
In Canada, glacial isostatic adjustment (GIA) is the dominant process to cause vertical motion of the land surface. A GIA model is presented herein that can be used to predict slope changes at given locations in North America where GIA is the primary cause for vertical motion. Uncertainty in Pleistocene ice cover and viscosity in the Earth’s mantle prevent one from picking a single GIA model from the literature to predict uplift rates in the region. Therefore, in this study, a range of mantle viscosity values as well as two different ice-loading histories are used in a forward model of the GIA process. The combination of viscosities and ice model that gives the best fit to recently available continuous and episodic GPS observations is assumed to provide the best prediction of slope changes in North America. This model can be used to quantify GIA-induced vertical deformation in local geomorphologic studies. We show that the predicted differential uplift rate in the Nelson River with respect to Lake Winnipeg reaches 1 mm/year over a 200 km distance using the model that best fits the GPS data whereas the ICE-5Gv1.2/VM2 model gives a slightly larger value. The difference in uplift rate between the northern and southern shore of Lake Michigan amounts up to 3 mm/year (slightly larger than the ICE-5Gv1.2/VM2 model), which could lead to a change in shorelines of tens of metres horizontally over a period of 100 years.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".