Application of ionospheric tomography to real‐time GPS carrier‐phase ambiguities Resolution, at scales of 400–1000 km and with high geomagnetic activity
Bibliographic record
Abstract
The influence of the ionosphere can be one of the main obstacles to GPS carrier phase ambiguity resolution in real‐time, particularly over long baselines. This is important to all users of GPS requiring sub‐decimeter positioning, perhaps in real time, especially with high geomagnetic activity or close to the Solar Maximum. Therefore, it is desirable to have a precise estimation of the ionospheric delay in real‐time, to correct the data. In this paper we asses a real‐time tomographic model of the ionosphere created using dual‐frequency phase data simultaneously collected with the receivers of a network of stations in the USA and Canada, with separations of 400–1000 km, during a period of high geomagnetic activity (Kp=6). When the tomographic ionospheric correction is included, the resolution on‐the‐fly (OTF) of the widelane double‐differenced ambiguities at the reference stations is nearly 100% successful for satellite elevations above 20 degrees, while the resolution of the L 1 , L 2 ambiguities at the rover is typically more than 80% successful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".