Bibliographic record
Abstract
The concept of Precise Point Positioning (PPP) using Global Navigation Satellite System (GNSS) technology was first introduced in 1976, however it took until the 1990s for PPP to generate interest amongst the greater GNSS community. Over the last two decades, dual-frequency PPP has been extensively researched, culminating in the availability of PPP post-processed correction products from organisations such as the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL), the International GNSS Service (IGS) and Natural Resources Canada (NRCan). With the advent of cost-effective, accurate, Real-Time Kinematic (RTK) positioning provided by an increasing number of Continuously Operating Reference Station (CORS) networks around the world, the focus of PPP has shifted to real-time or near real-time solutions. A real-time or near real-time PPP solution would potentially allow for a viable alternative to RTK solutions in some circumstances. However, several limitations still remain, primarily the long convergence times needed to resolve ambiguities, currently restricting the use of PPP for real-time applications. This paper provides a brief history of the development of real-time PPP and reviews the recent advances made in PPP with an emphasis on the development of a real-time or near real-time PPP solution.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.031 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".