Bibliographic record
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 WAAS users is the ionospheric feature called storm enhanced density (SED). In order to assess the WAAS performance – including both the WAAS positioning accuracies and errors in the WAAS ionosphere models - the University of Calgary operates the Canadian GPS Network for Ionosphere Monitoring (CANGIM). This network consists of a number of sites in Canada, augmented by several sites in the northern United States, which collect WAAS geostationary downlink messages. These messages are collected continuously and decoded (post-mission) at University of Calgary. Localization schemes have been developed to compute WAAS corrections for any location in North America. In this paper, issues associated with obtaining optimal WAAS accuracies under increased ionospheric conditions are investigated. An extensive analysis of WAAS positioning accuracies is conducted for North America during the intense ionospheric storm events, using data from both IGS and CORS reference stations with localized WAAS corrections. By estimating WAAS position errors at hundreds of independent reference stations across the United States and Canada, the spatial variations in positioning accuracies are determined and correlated with development of local ionospheric effects. It is observed that the sparse WAAS reference network may not adequately model local ionospheric effects during increased ionospheric activity. Positioning accuracies are degraded by a factor of ten in regions extending several thousand kilometers. Such position errors can persist for several hours. Analysis of future system enhancements, such as the CWAAS, are investigated to improve positioning accuracies and service availability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".