Space weather predictions service for safety-critical GPS applications
Bibliographic record
Abstract
Enhanced ionospheric effects may exist during space weather events, leading to degradations in GPS performance and positioning accuracies. This issue is a concern for reliable operation of safety-critical GPS systems, such as marine DGPS services or Satellite-Based Augmentation Systems (SBAS) for aviation applications. These storm-related effects tend to peak in the years following solar maximum, and will continue to be a concern for GPS applications over the next few years (2002-2003). In order to provide timely predictions of space weather events, we have recently investigated an ionospheric warning and alert system for GPS applications. Predictive capabilities are based on space weather parameters provided by the United States Space Environment Center at NOAA. The impact of ionospheric activity on GPS performance has been quantified using several years of GPS data from the North American sector, with a focus on marine and aviation applications. We have established strong correlations between GPS performance and various ionospheric phenomena, and we are able to provide space weather predictions for GPS users up to six hours in advance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.170 | 0.131 |
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".