Optimal locations for GPS measurements in North America and northern Europe for constraining Glacial Isostatic Adjustment
Bibliographic record
Abstract
We determine the optimal location in North America and Fennoscandia for uplift rate or tangential velocity data that will be useful for addressing ice sheet thickness, lithospheric thickness, lateral viscosity variation and background viscosity profile in the lower mantle. An optimal location is defined by where sensitivity lies above the current accuracy of GPS measurements. The approach here is different from previous studies that compute sensitivity kernels for viscosity perturbations within a small volume of the mantle. The advantage of the current approach is that the total effect of 3-D lateral heterogeneity in the mantle related to seismic tomography of the whole mantle can be studied. The sensitivity of ice sheet models and lateral lithospheric thickness variations are also studied. Our results show that in North America more permanent GPS stations are needed in northern Canada especially in a region west of the Hudson Bay until the Rocky Mountains. In Fennoscandia, the GPS network is almost adequate, but it should be extended to the last known GIA-affected areas in the Russian part of East Europe and to Central Europe. In addition, we show locations of prospective GPS sites that are sensitive to all four parameters (ice sheet thickness, lithospheric thickness, lateral viscosity variation and background viscosity profile in the lower mantle) and locations that are sensitive to only one, two or three parameters. Thus, the results are useful for the inversion of one individual parameter or for the separation of the effects of two or more parameters in inversions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".