Statistical Comparison on Accuracies of Web-Based Online PPP Services
Bibliographic record
Abstract
The precise point positioning technique (PPP), an absolute positioning method, is widely used in geodetic point positioning. This study investigates the accuracy of the technique through statistical comparisons of the root mean square errors (RMSE), which are calculated with coordinates obtained from online PPP services. For this purpose, we carried out a two-stage investigation. First, we chose two days, a high geomagnetic activity day and a quiet one. During the high geomagnetic activity day, the Kp index went above 7, and the Dst was below −170nt. We uncovered the effect of geomagnetic activity by using the Automatic Precise Positioning Service (APPS), Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP), and magic Global Navigation Satellite System (magicGNSS) PPP services. The results show that the three-dimensional (3D) RMSE are ∼1.5 times higher during the high geomagnetic activity day during 1-, 2-, 4-, and 6-h session durations, than in the quiet day. However, when the session duration increases to 24 h, the effects of geomagnetic activities are significantly eliminated. In the second stage, we chose 31 consecutive days without significant geomagnetic activities and obtained PPP-derived coordinates for 1-, 2-, 4-, 6-, and 24-h from APPS, CSRS-PPP, magicGNSS, and Trimble real-time extended (RTX). We compared the PPP-derived coordinates with Australian Online GPS Processing Service (AUSPOS)- and Online Positioning User Service (OPUS)-derived reference coordinates. As a result, we determined that, as the session duration increases, the 3D RMSE decreases, and therefore the position accuracy increases. In terms of 3D RMSE, CSRS-PPP yielded the best results in all scenarios. Finally, we concluded that 3D RMSE decreased by approximately 55.8%, 18.1%, 7.7%, and 6.2%, when session durations increased from 1 h to 2 h, 2 to 4 h, 4 to 6 h, and 6 to 24 h, respectively.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".