Performance Evaluation of the Wide Area Augmentation System for Ionospheric Storm Events
Bibliographic record
Abstract
Abstract. One of the greatest challenges in developing accurate and reliable satellite-based augmentation systems (SBAS) is modeling of ionospheric effects. Wide area GPS networks are generally sparse (station spacings of 500-1000 km), and ionosphere models can suffer degraded performance in regions where large spatial gradients in total electron content (TEC) exist. Of particular concern for Wide Area Augmentation System (WAAS) users is the feature called storm enhanced density, which is associated with large TEC gradients at mid-latitudes. This effect is a significant source of error in the WAAS correction models. The Canadian GPS Network for Ionosphere Monitoring (CANGIM) consists of three GPS reference stations in western Canada, augmented by two additional sites in the northern United States. In addition to measures of ionospheric activity, WAAS messages are collected continuously at these sites and decoded (post-mission) at University of Calgary. Localization schemes have been developed to compute WAAS ionosphere corrections for any location in North America. In this paper, performance of the broadcast WAAS ionosphere model is quantified through comparison with truth data from over 400 GPS reference stations in North America. WAAS ionosphere model accuracies throughout North America are evaluated for intense storm events, and compared with WAAS Grid Ionosphere Vertical Error (GIVE) bounds. Limitations in the WAAS ionosphere model are identified for enhanced ionospheric activity and, in particular, the storm enhanced density phenomenon. Key words: Ionosphere, WADGPS, WAAS, GPS, positioning, geomagnetic storm 1
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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 | 0.001 |
| 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 | 0.001 |
| 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".