RTK-Quality Positioning With Global Precise Point Positioning Corrections
Bibliographic record
Abstract
Abstract Global navigation satellite system (GNSS) precise point positioning (PPP) has potential as an alternative or replacement for real-time kinematic (RTK) processing. In this work, we reached for RTK levels of performance without the need for local information through PPP (i.e., centimeter-level positioning that was reached near-instantaneously). This work makes use of information currently available from processing signals from global positioning system (GPS), Galileo, BeiDou-2/3, and GLONASS by fixing ambiguities for the first three constellations on all available frequencies. This processing was done using a four-frequency, four-constellation uncombined decoupled clock model (DCM) that has been expanded as part of this work. The results were tested on 1448 global datasets and showed that instantaneous convergence on average to 2.5 cm error can be achieved for 81% of the stations. These findings were reinforced by the results of epoch-by-epoch processing, as an average of 80% of all single epochs converged below 2.5 cm error at 1σ, as opposed to less than the 0.5% typically observed for classic PPP.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".