Variance-Covariance Modeling of Atmospheric Errors for Satellite-Based Network Positioning
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Bibliographic record
Abstract
ABSTRACT: This paper investigates the problem of variance-covariance modeling of atmospheric errors for the purposes of improving the accuracy of positioning using global navigation satellite systems, as well as improving estimates of the accuracy of such positioning. A method of modeling all variances and cross-correlations among observations made in a network of positioning receivers is presented. This is done by generating a theoretical model of covariance behavior based on the physical nature of tropospheric and ionospheric errors, respectively, and then using data observed at a network of reference receivers to derive key parameters of the theoretical model. The applicability of this method is demonstrated for a network of 10 receivers with a total extent of 250 km. It is shown that proper modeling of covariances improves positioning accuracy an average of 22 percent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it