An Enhanced Real-Time Regional Ionospheric Model Using IGS Real-Time Service (IGS-RTS) Products
Bibliographic record
Abstract
Recently, the International Global Navigation Satellite System (GNSS) Service (IGS) has launched the Real-Time Service (IGS-RTS). The RTS products enable real-time precise positioning applications. For single-frequency Real-Time Precise Point Positioning (RT-PPP), ionospheric delay mitigation is a major challenge. To overcome this challenge, we developed a Real-Time Regional Ionospheric Model (RT-RIM) over Europe using the RTS satellite orbits and clock products. The model has spatial and temporal resolution of 1° × 1° and 15 minutes, respectively. Global Positioning System (GPS) observations from 60 IGS and EUREF reference stations are processed using the Bernese 5·2 PPP module in order to extract the Real-Time Vertical Electron Content (RT-VTEC). The PPP convergence time and positioning accuracy using the RTS products is estimated and compared with dual frequency PPP and single-frequency PPP obtained through the combined rapid IGS Global Ionospheric Maps (IGS-GIM) over three consecutive days under high solar activity and one of them under active geomagnetic activity. The results show that the proposed model improves PPP accuracy and convergence time under the mid-latitude region about 40%, 55% and 40% for the horizontal, height and three-dimensional (3D) components respectively in comparison with the IGS-GIM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".