Exploring Thermospheric Disturbance Patterns Through Space‐Borne Accelerometer Measurement Errors: A Weighted Accelerometer 1B Dataset of GRACE C
Bibliographic record
Abstract
Abstract Satellite measurements are essential for understanding Earth's complex system, yet they often lack a reliable a‐priori covariance matrix. This study presents a new methodology to enhance the reliability of satellite measurements by deriving experimental covariance matrices from the original observation. We focus on the accelerometer measurements (1A dataset) from the GRACE (Gravity Recovery and Climate Experiment) C satellite. Using autocorrelation analysis, we create a block‐diagonal covariance matrix for the 1A dataset. We then apply a low‐pass Gaussian filter that integrates this covariance matrix into the least squares estimation, resulting in a refined 1B dataset that minimizes spikes and spurious accelerations while preserving measurement error. Our variance analysis uncovers disturbances linked to geomagnetic storms and the satellite's transitions through Earth's shadow and terminator, with fluctuations notably peaking during the equinoxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".