Network Real-Time Kinematic Performance Analysis Using Rtcm 3.0 and the Southern Alberta Network
Bibliographic record
Abstract
The RTCM 3.0 data transmission format is introduced and described as it applies to multiple reference station real-time kinematic differential GPS positioning, or network RTK. The new format provides a more modern and flexible message structure that accommodates network RTK data transmission, while requiring up to 80% less bandwidth for the transmission of RTK correction messages. Based on this reduced bandwidth, we investigated moving the correction interpolation step of the network RTK procedure from the network to the rover user. The RTCM 3.0 format is implemented and tested in both network and user software. Three interpolation methods are implemented and compared with single baseline processing using real data collected using reference stations from the Southern Alberta Network. Under moderate ionospheric conditions, the network RTK solution outperforms the single baseline approach in both the observation and position domains. The three interpolation methods are found to be comparable. Under severe ionospheric conditions, network ambiguity resolution becomes difficult, making the network corrections unreliable. It is recommended that a network ambiguity resolution status flag be added to the RTCM 3.0 network correction message format to alert users when network corrections are unreliable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".