Evaluating the Performance of the Static PPP-AR in a Forest Environment
Bibliographic record
Abstract
Forest environment and topographic obstacles tend to reduce the positioning performance of precise point positioning (PPP) with ambiguity resolution (AR) and may even prevent radio signals from reaching the global navigation satellite systems (GNSS) antenna. In this study, we investigated the positioning performance of PPP-AR in a forest environment in terms of the crown closure ratios, session duration (1-, 2-, 3-, and 6-h), and different satellite constellations [i.e., the global positioning system (GPS)-only and GPS+GLONASS combined satellites]. For this purpose, three GNSS receivers were used to make measurements at three test points in areas with crown closure ratios of 0%, 38%, and 87%. The data were evaluated using the PRIDE PPP-AR software and Canadian Spatial Reference System-PPP (CSRS-PPP). The experiments revealed that the inclusion of the GLONASS observations in the GPS-only solutions did not obviously improve the positioning error and accuracy with closure ratios of 0% and 38%. However, the improvements became more dramatic when the closure ratio increased to 87%. Furthermore, in the horizontal components, an accuracy of 10 cm can be achieved with at least a 2-h session, whereas for the up component, this level of accuracy can only be achieved with a 3-h session. While the PRIDE PPP-AR was able to achieve a 3D positioning performance of 1 cm with the combined GPS+GLONASS satellites, this accuracy level remained at 8 cm in CSRS-PPP.
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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 | 0.001 | 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 | 0.000 |
| 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".