Ionosphere-Nullification Technique for Long-Baseline Real-Time Kinematic Applications
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT: One of the major challenges in resolving ambiguities for longer baselines is the presence of unmodeled ionospheric delays. In this paper, we describe a new ionospheric approach that does not rely on the convergence of an ionospheric parameter, and that instantaneously nullifies the effect of the differential ionospheric delay in the ambiguity search process. Although this approach was originally developed for single-baseline RTK over long distances in kinematic mode, it can be used for network RTK when requiring extrapolation of the differential ionospheric corrections for a rover located outside the network. It can also be used in cases where the rover located inside the network is experiencing a local anomaly in the differential ionospheric delays. The performance of the ionosphere-nullification technique was demonstrated on an approximately 74 km ferry route across the Bay of Fundy in eastern Canada. For both static and kinematic tests over the 74 km baseline, a few mm mean differences were observed in each Cartesian component, and the comparison 1s̀ noise level was at the few cm level.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it