Bibliographic record
Abstract
Gravity Recovery and Climate Experiment (GRACE) satellite has been producing many scientific results concerning seasonal changes in the Earth’s gravity field since its launch in March 2002. On the other hand, global, regional, and local scale deformations have been continuously investigated using Global Positioning System (GPS) over the last two decades. Here we try to compare velocity field from GPS and non-seasonal gravity changes from GRACE. We take two different approaches, i.e. those in the wave number domain and in the space domain. In the first approach we found that it is approximately up to degree 15 in terms of the Stokes’ coefficients that secular changes of gravity well exceed measurement errors in the gravity field recovered by GRACE. As for GPS, we found it rather difficult to extract crustal deformation field expressed by spherical harmonics due mainly to the inadequate GPS station coverage, i.e. we could recover such field for only up to degree 4. Correlation between the Stokes’ coefficients from the two techniques did not show good significance. Then we tried to compare GRACE and GPS in space domain. From the secular gravity change map up to degree 10 recovered by GRACE, significant regional gravity changes have been observed in numbers of areas, namely Hudson Bay (Canada), Alaska, Greenland, southeastern Africa, Southeast Asia, and in the southern part of South America. For regions with recent and past ice melting, we compared gravity changes and GPS velocities and confirmed that these changes reflect elastic and viscous responses of the solid earth to ice melting. Gravity changes in Southeast Asia were found to reflect the coseismic jump in the gravity field associated with the 2004 Sumatra-Andaman Earthquake rather than climate-driven secular decrease.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".