Testing a Multi-Reference GPS Station Network for OTF Positioning in Brazil
Bibliographic record
Abstract
Differential GPS has the capability to provide cm-level positioning accuracy, as long as the carrier phase ambiguities are resolved on-the-fly (OTF) to integer values. Existing methods are based on the use of a single fixed reference station located in the vicinity of the rover. The maximum distance allowed between the reference station and user is generally limited to within 50 km due to effects from the atmosphere and orbit. The number of reference stations can be increased to extend the coverage. However, the installation and maintenance of such stations and their data transmission links is operationally complex and expensive. The optimal use of existing stations is therefore of utmost importance to maximize cost effectiveness. A novel and unique method developed at the University of Calgary uses all available reference stations to generate regional code and carrier phase corrections, which can be transmitted to the user in order to resolve integer ambiguities OTF over the region. One of the major advantages of this method is to increase the coverage within which successful OTF ambiguity resolution is possible. The Brazilian Network for Continuous Monitoring of GPS (RBMC) is an active geodetic reference network functioning in Brazil since 1997. As the distances between the established stations are too long for carrier-phase positioning, a 5-day densification campaign was carried out in August 1999 covering a 700x700-kilometer region. The generated data was used to test the feasibility of the multi-reference station approach in a region strongly affected by the ionosphere, under the equatorial anomaly. During the campaign, a solar eclipse occurred. Despite the fact that the zone of totality was mainly seen over Europe and Asia, this event added an interesting feature to the collected data. Results and analysis using the regional code and carrier phase corrections are presented and compared to the single reference receiver case to demonstrate the improvement achieved by this method under the conditions pointed out.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".