How well can online GPS PPP post-processing services be used to establish geodetic survey control networks?
Bibliographic record
Abstract
Establishing geodetic control networks for subsequent surveys can be a costly business, even when using GPS. Multiple stations should be occupied simultaneously and post-processed with scientific software. However, the free availability of online GPS precise point positioning (PPP) post-processing services offer the opportunity to establish a whole geodetic control network with just one dual-frequency receiver and one field crew. To test this idea, we compared coordinates from a moderate-sized (~550 km by ~440 km) geodetic network of 46 points over part of south-western Western Australia, which were processed both with the Bernese v5 scientific software and with the CSRS (Canadian Spatial Reference System) PPP free online service. After rejection of five stations where the antenna type was not recognised by CSRS, the PPP solutions agreed on average with the Bernese solutions to 3.3 mm in east, 4.8 mm in north and 11.8 mm in height. The average standard deviations of the Bernese solutions were 1.0 mm in east, 1.2 mm in north and 6.2 mm in height, whereas for CSRS they were 3.9 mm in east, 1.9 mm in north and 7.8 mm in height, reflecting the inherently lower precision of PPP. However, at the 99% confidence level, only one CSRS solution was statistically different to the Bernese solution in the north component, due to a data interruption at that site. Nevertheless, PPP can still be used to establish geodetic survey control, albeit with a slightly lower quality because of the larger standard deviations. This approach may be of particular benefit in developing countries or remote regions, where geodetic infrastructure is sparse and would not normally be established without this approach.
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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 | 0.032 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".