Analysis of a High Accuracy Service based on JPL’s Global Differential GPS
Bibliographic record
Abstract
<title>Abstract</title> In the current global navigation satellite system (GNSS) context, with several constellations offering high accuracy services (HAS), we have evaluated a potential HAS for GPS based on JPL’s Global Differential GPS (GDGPS) system. This HAS also provides corrections for Galileo and GLONASS. In this paper, we specifically consider the scenario in which satellite corrections are delivered to users through the internet, similar to one style of access used for Galileo HAS. The GDGPS-based HAS described herein consists primarily of high-quality satellite orbit and clock corrections and currently excludes code and phase biases. Corrections are provided in two parallel variations: one stream supporting GPS and Galileo, and the other supporting GPS and GLONASS. Each variation is provided in two redundant instances for robustness, giving a total of four streams. Our results, including PPP solutions based on these products, attest to the quality of the corrections. PPP results show good performance, comparable to solutions generated based on real-time CNES products and better than solutions generated based on internet-based Galileo HAS products. For example, based on processing over 2,000 independent three-hour data sets, both the GDGPS-based HAS GPS+GAL streams and the CNES stream achieved post-convergence horizontal rms below 20 cm for 97% of data sets and below 10 cm for 80%. In contrast, only 86% of Galileo HAS-based solutions have post-convergence horizontal rms below 20 cm, and only 47% have rms below 10 cm. Overall, these results suggest a promising method of implementing a GDGPS-based HAS that might augment GPS, Galileo, and GLONASS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".