Products and Applications for Precise Point Positioning - Moving Towards Real-Time
Bibliographic record
Abstract
The timely availability of global precise GPS satellite orbit and clock products is enabling the development of precise point positioning (PPP) user applications. Based on the processing of un-differenced pseudorange and carrier phase observations from a single GPS receiver, positioning with centimeter to decimeter accuracy can be attained globally. The PPP approach brings great flexibility to GPS field operations, reduces labor and equipment costs, and simplifies operational logistics by eliminating the need for observation differencing and simultaneous tracking at another location. Seamless integration of the survey results into a global reference frame is another advantage over the differential approach. This paper summarizes the availability of some post-mission precise orbit/clock products for public access and the performance of PPP in static and kinematic modes using single and dual frequency observations . Numerical results are presented showing user positioning accuracy in post-mission and in real-time simulations using NRCan’s On-Line PPP Service and P3, a software package developed at the University of Calgary. From the perspective of GPS correction provision, the impact of tracking network coverage and observation types processed on the correction precision is analyzed with a state-space model implementation using wide-area and global tracking networks. Finally, current limitations of GPS correction sources and the PPP approach are summarized and potential areas for further research and development are proposed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.035 |
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".