Exploring the role of PPP–RTK network configuration: a balance of server budget and user performance
Bibliographic record
Abstract
Abstract With atmospheric corrections generated from the server, precise point positioning real-time kinematic (PPP–RTK) can achieve high-precision solutions in a fast convergence. PPP–RTK users are concerned about how to use the corrections and the level of performance that can be achieved; thus, our research has focused on correction methods, a priori stochastic modeling, and positioning performance evaluation. Conversely, it is crucial for the server to improve the precision of corrections provided and to consider the balance between cost, computation burden and user performance, especially for commercial applications. We use different scales of the national GPS network of France to generate ionospheric and tropospheric corrections, and corresponding uncertainty information is generated by establishing error functions with respect to an inter-station distance. The quality of corrections and corresponding user performance are analyzed with inter-station distances varying from 22 to 251 km. The results show that the precision of atmospheric corrections can be improved with an increasing number of stations in the network, but the improvement is not significant when the inter-station distances are smaller than 50 km. Regarding user performance, compared to conventional PPP solutions with ambiguity resolution, the convergence time can be reduced by a maximum of 93% and 85% in the horizontal and vertical components, respectively, when the inter-station distance is about 23 km. However, a station spacing within 100 km can still support a 3-min convergence; thus, a balance of server budget and user performance should be considered instead of a dense network.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".