Optimal locations for absolute gravity measurements and sensitivity of GRACE observations for constraining glacial isostatic adjustment on the northern hemisphere
Bibliographic record
Abstract
Gravity rate of change is an important quantity in the investigation of glacial isostatic adjustment (GIA). However, measurements with absolute and relative gravimeters are laborious and time-consuming, especially in the vast GIA-affected regions of high latitudes with insufficient infrastructure. Results of the Gravity Recovery And Climate Experiment (GRACE) satellite mission have thus provided tremendous new insight as they fully cover those areas. To better constrain the GIA model (i.e. improve the glaciation history and Earth parameters) with new gravity data, we analyse the currently determined errors in gravity rate of change from absolute gravity (AG) and GRACE measurements in North America and Fennoscandia to test their sensitivity for different ice models, lithospheric thickness, background viscosity and lateral mantle viscosity variations. We provide detailed sensitivity maps for these four parameters and highlight areas that need more AG measurements to further improve our understanding of GIA. The best detectable parameter with both methods in both regions is the sensitivity to ice model changes, which covers large areas in the sensitivity maps. Also, most of these areas are isolated from sensitive areas of the other three parameters. The latter mainly overlap with ice model sensitivity and each other. Regarding existing AG stations, more stations are strongly needed in northwestern and Arctic Canada. In contrast, a quite dense network of stations already exists in Fennoscandia. With an extension to a few sites in northwestern Russia, a complete station network is provided to study the GIA parameters. The data of dense networks would yield a comprehensive picture of gravity change, which can be further used for studies of the Earth's interior and geodynamic processes.
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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 | 0.001 | 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".