Advancements in the GRACE and GRACE‐FO Gradiometer Mode
Bibliographic record
Abstract
Abstract The global gravitational gradient field has not been observed since the decommission of GOCE in 2013. Based on the foundational work of Peidou and Pagiatakis (2019, https://doi.org/10.1029/2018jb016382 ), we advance the concept of GRACE gradiometer mode (GM) for the purpose of using GRACE, GRACE‐FO and future gravity space missions as “gradiometer missions.” Certainly, the GRACE missions have never carried on board a gradiometer; it is the concept of GM that creates a fictitious gradiometer system very similar to GOCE, only the GRACE “gradiometers” have long and variable baselines, an unprecedented paradigm for space‐based gravitational gradiometry that extends the bandwidth of available GOCE gradient solutions. In this contribution, we develop a new configuration for GM that views an individual satellite as the “gradiometer” by directly using Level 1A accelerometer measurements at a 10 Hz sampling rate. We apply the new method in geodynamically active regions around the globe, and we demonstrate that using GRACE‐C as a “gradiometer” in the single‐satellite gradiometer mode (SS‐GM) produces higher‐fidelity gravitational gradient estimates, clearly delineating tectonic plate boundaries and subduction zones in the Himalayas and North Africa regions, the Aleutian trench, the Java trench, and the Peru‐Chile trench. Over Canada, we see the delineation of the Canadian shield, and the effect of glacial isostatic adjustment is apparent. We also observe well‐known signals resembling terrestrial water storage changes in Africa, among others, demonstrating the usefulness of the GM for a wide variety of geoscience applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".