THE PERFORMANCE ANALYSIS OF THE POST-MISSION WEB-BASED STATIC AND KINEMATIC PPP-AR SERVICE
Bibliographic record
Abstract
The use of the Precise Point Positioning (PPP) technique has become very advantageous with the development of GNSS positioning technology. It is possible to get highly accurate position information without the need of any reference station data using the PPP technique. However, there are various factors that affect the accuracy of PPP solutions, including the initial phase ambiguity solution type, which can be fixed or float, atmospheric effects, observation length, used satellite systems, and used precise products. The Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) service, one of the online PPP services, was updated on October 20th, 2020, and upgraded to version 3, capable of the Ambiguity-Fixed (PPP-AR) solution. Prior to this date, the service had offered the Ambiguity-Float (PPP-Float) solution. In this study, it is aimed to investigate the effect of using different satellite systems (GPS, GPS&GLONASS), length of observation time, static/kinematic processing modes, and initial phase ambiguity solution types on PPP accuracy. The daily observation data of ANKR, ISTA, IZMI, MERS, and KRS1 IGS GNSS stations located within the borders of Türkiye, divided into different sub-sessions (1-hour, 2-hours, 4-hours, 8-hours, and 12-hours) were processed using CSRS-PPP web-based service as PPP-Float before the update and PPP-AR after the update. As a result of the comparison, the combined use of GPS & GLONASS satellite systems instead of using GPS satellites alone has increased horizontal and vertical accuracy in both static/kinematic PPP-Float and PPP-AR solutions. Considering the static solutions, horizontal and vertical position accuracies increase as the observation time increases in both ambiguity solution methods using different constellations. In the case of comparison of the ambiguity solution methods, it was found that the PPP-AR approach offered higher accuracy than the PPP-Float in all solution cases.
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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 | 0.002 |
| 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".