SBAS/EGNOS for Maritime
Bibliographic record
Abstract
The Global Navigation Satellite System (GNSS) has become the primary means of obtaining Position, Navigation, and Timing (PNT) information at sea. The current capabilities of the Global Positioning System (GPS) constellation, although adequate for ocean navigation, have some shortfalls for coastal navigation: some user communities have a need for enhanced performance and they can benefit from the available “augmentation” techniques, resulting in improved GPS performance. Nowadays, the users can take advantage of Satellite-Based Augmentation Systems (SBASs). The maritime domain has been used SBAS for several years and it is supported by GNSS receivers used in the recreational and professional sectors. The SBAS/European Geostationary Navigation Overlay Service (EGNOS) can be used to complement the differential GNSS (DGNSS) for the provision of enhanced accuracy and integrity information with additional benefits. There are different possible solutions for the transmission of SBAS/EGNOS information to maritime users, considering that the corrections can be available from different transmission means. The different options for the use of SBAS for maritime navigation, the benefits brought to mariners, as well as the associated regulations, standardization and service provision aspects, are presented in this article.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".