Effect of the GRACE satellite mission on gravity field studies in Fennoscandia and the Baltic Sea region
Bibliographic record
Abstract
It is customary to utilize the Earth's artificial satellites for detecting long-wavelength components of the Earth's gravity field.The tracking data of the GRACE twin-satellites are the basis of the new geopotential model GGM01, released by the Centre for Space Research at the University of Texas in July 2003.The present paper assesses the quality of the GGM01 model through comparisons with an earlier geopotential model (EGM96).The method of spherical harmonic expansions is used in numerical investigations.The results of evaluation in Fennoscandia and the Baltic Sea region illuminate the discrepancies between the long-wavelength contributions of the models, which may reach several decimetres in the geoidal heights.Thus, even in the gravimetrically well studied area like the Baltic Sea region, the new satellite gravity missions may improve the gravity data significantly.Tests with high-precision GPS-levelling data indicate the offsets between global geoid models and national vertical datums in the Baltic Sea region.The gravity anomaly grid and the GGM01 model are utilized in the computation of the Estonian gravimetric geoid model by the least squares modification of Stokes' formula.The new model EST-03 is fitted to a set of 26 high-precision GPS-levelling points, yielding a root mean square error of 3 cm for the post-fitting residuals.This order of discrepancies is sufficient for many practical and scientific 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".