A Comparative Study of Precise Point Positioning (PPP) Accuracy Using Online Services
Bibliographic record
Abstract
Abstract Precise Point Positioning (PPP) is a technique used to determine the position of receiver antenna without communication with the reference station. It may be an alternative solution to differential measurements, where maintaining a connection with a single RTK station or a regional network of reference stations RTN is necessary. This situation is especially common in areas with poorly developed infrastructure of ground stations. A lot of research conducted so far on the use of the PPP technique has been concerned about the development of entire day observation sessions. However, this paper presents the results of a comparative analysis of accuracy of absolute determination of position from observations which last between 1 to 7 hours with the use of four permanent services which execute calculations with PPP technique such as: Automatic Precise Positioning Service (APPS), Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP), GNSS Analysis and Positioning Software (GAPS) and magicPPP - Precise Point Positioning Solution (magicGNSS). On the basis of acquired results of measurements, it can be concluded that at least two-hour long measurements allow acquiring an absolute position with an accuracy of 2-4 cm. An evaluation of the impact on the accuracy of simultaneous positioning of three points test network on the change of the horizontal distance and the relative height difference between measured triangle vertices was also conducted. Distances and relative height differences between points of the triangular test network measured with a laser station Leica TDRA6000 were adopted as references. The analyses of results show that at least two hours long measurement sessions can be used to determine the horizontal distance or the difference in height with an accuracy of 1-2 cm. Rapid products employed in calculations conducted with PPP technique reached the accuracy of determining coordinates on a close level as in elaborations which employ Final products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".