Performance investigation of GLONASS in the static PPP technique with independent short measurement times using online processing services
Bibliographic record
Abstract
The precise point positioning (PPP) technique, which is still being developed, provides position accuracy at the centimetre (cm) level and is widely employed in scientific research. In the PPP technique, cm-level accuracy can be achieved by evaluating raw data obtained from a single Global Navigation Satellite Systems (GNSS) receiver using precise satellite orbit and clock correction data and other parameters. The majority of studies in the literature are based on 24-hour data obtained from the International GNSS Service (IGS) and similar stations. However, there are fewer articles in which analyzes based on short-term measurements are taken. In this study; the effect of GLONASS on the static PPP technique was investigated with independent short measurement times. For this purpose, measurements were made at 7 different test points on consecutive days using a single GNSS receiver. A 4-hour static measurement was made at each test point. The data obtained were processed in two different scenarios, only GPS and GPS + GLONASS using the Canadian Spatial Reference System – PPP (CSRS-PPP) and Trimble RTX online process software. The processes were completed at 4, 2, 1, and 0.5 h. As a result of the analysis, it has been observed that GLONASS improves the results by 76%, but negatively affects some solutions (24%). It was also observed that GLONASS drastically reduced the outlier values. With this study, it is aimed to show the accuracy that users who make short-term measurements with a single GNSS receiver can be achieved in the static PPP technique by using GPS + GLONASS systems, with repeated measurements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".