GOCE Gradiometer Measurements Response to Ionospheric Dynamics
Bibliographic record
Abstract
Abstract With the launch of dedicated satellite gravity missions, starting with CHAllenging Minisatellite Payload (CHAMP) in 2000, with Gravity Recovery and Climate Experiment (GRACE) in 2002, and Gravity field and steady‐state Ocean Circulation Explorer (GOCE) in 2009, the accuracy and spatial resolution of the Earth's global gravity field models have been improved. Highly sensitive accelerometer measurements have not only been useful for gravity field modeling but have also been contributing to the studies of thermospheric dynamics. While improving the sensitivity of the accelerometer measurements, the new instrumentation used on board GOCE brings different challenges in understanding the data and developing sophisticated data processing. Our analyses reveal that the GOCE gravitational gradient measurements were affected by highly variable ionospheric dynamics that did not only degrade the quality of the GOCE Electrostatic Gravity Gradiometer (EGG) measurements but also proved that some characteristics of ionospheric dynamics can be measured by GOCE accelerometers and other Low Earth Orbiters. In this paper, we show how GOCE‐retrieved neutral winds respond to main ionospheric currents and we develop the impulse‐response relation between intense ionospheric dynamics (plasma drift) represented by Poynting energy flux and the gravity gradiometer tensor trace disturbances observed over the north geomagnetic polar region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".