An assessment of the interoperability of PPP-AR network products
Bibliographic record
Abstract
Integer ambiguity resolution of carrier-phase measurements from a single receiver can be implemented by applying additional satellite corrections (products) to mitigate unmodelled satellite equipment delays. Interoperability of different PPP-AR products would allow the PPP user to transform independently generated PPP-AR products to obtain multiple fixed solutions of comparable precision and accuracy with limited changes required to user PPP measurement processing software. The ability to provide multiple solutions would increase the reliability of the solution for, e.g., real-time processing; if there were an outage in the generation of one set of PPP-AR products, the user could instantly switch streams to a different provider. There are currently three main public providers of real-time products that enable PPP-AR. These include School of Geodesy and Geomatics at Wuhan University (SGG-WHU), Natural Resources Canada (NRCan) and Centre National d’Etudes Spatiales (CNES). The presented research examines the PPP-AR products generated from the FCB (Fractional Cycle Bias) model and IRC (Integer Recovery Clock) model that have been transformed into the DC (Decoupled Clock) format and applied within the PPP user solution. Interoperability of the different PPP-AR products is a challenging task due to the public availability of different quality of products, limited literature documenting the conventions adopted within the network solution of the providers and unclear definitions of the corrections. The novelty of the research is in the analysis of using the transformed products. The convergence time (time to first fix and time to a pre-defined performance level), position precision (repeatability), position accuracy and solution outliers are examined. Equivalent performance was noted utilizing the different methods. Of the four solutions, FCB products had the highest accuracy. This is attributed to the products being generated using final IGS orbit and clock products. To confirm this, FCBs generated using GRG orbit and clock products were also examined and comparable performance was observed between the FCBs and IRC (GRG) products. The least accurate solution was obtained using the IRC (CNT) products, which was due to the products being archived real time products.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".