A Comparison of Single Reference Station, Correction-Based Multiple Reference Station, and Tightly Coupled Methods using Stochastic Ionospheric Modelling
Bibliographic record
Abstract
Abstract. The multiple reference station approach to carrier phase-based positioning uses a network of GPS reference stations to model the correlated errors in a geographic region. This paper compares two methods for multiple reference station positioning under a low and a high level of ionosphere. The first method tested is the conventional method for multiple reference station positioning, which is usually a three-step process, namely (1) estimation of the carrier phase ambiguities in the network, (2) prediction of the measured network errors at the location of the rover, and (3) application of the corrections in a practical format. The second method is called the tightly coupled or in-receiver approach, which uses the data from the rover and integrates it with the network solution to better model the effect of the ionosphere. In this approach there are no explicit corrections. These two methods are compared with the single reference station approach for data from two days collected from the Southern Alberta Network in Canada, a medium scale network with inter-stations distance of 34 to 59 km.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".