Precise Point Positioning using Multi-Constellation GNSS Observations for Kinematic Applications
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Traditional precise point positioning (PPP) is commonly based on un-differenced ionosphere-free linear combination of Global Positioning System (GPS) observations. Unfortunately, for kinematic applications, GPS often experiences poor satellite visibility or weak satellite geometry in urban areas. To overcome this limitation, we developed a PPP model, which combines the observations of three global navigation satellite systems (GNSS), namely GPS, GLONASS and Galileo. Both un-differenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase GNSS measurements are processed. The performance of the combined GNSS PPP solution is compared with the GPS-only PPP solution using a real test scenario in downtown Kingston, Ontario. Inter-system biases between GPS and the other two systems are also studied and obtained as a byproduct of the PPP solution. It is shown that the addition of GLONASS observations improves the kinematic PPP solution accuracy in comparison with that of GPS-only solution. However, the contribution of adding Galileo observations is not significant due to the limited number of Galileo satellites launched up to date. In addition, BSSD solution is found to be superior to that of traditional un-differenced model.
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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 | 0.000 |
| 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