Bibliographic record
Abstract
WAAS enhances the GPS standard positioning service by providing sufficient integrity, accuracy, availability and continuity for use in commercial aviation. The system provides en-route through non-precision approach (LNAV), Lateral NAVigation / Vertical NAVigation (LNAV/VNAV), and Localizer Performance with Vertical guidance (LPV) approach capabilities. But WAAS is more than a navigation system for pilots. Most any GPS receiver you buy today is WAAS enabled allowing everyone from hikers and bikers to surveyors, farmers and rescue workers to enjoy the benefits of improved accuracy and integrity in their day to day activities. Over the last 3 years (2005-2008), WAAS has undergone significant expansion: adding reference stations in Alaska, Mexico and Canada, upgrading processing software, and replacing the legacy Geostationary Earth Orbit (GEO) satellites with new satellites that are positioned to give dual signal coverage in North America. GEO satellites serve as both the source of the WAAS correction message and as additional ranging sources which are always in view of the service region. This paper shows the positive benefits of using GEO satellites as precision approach quality ranging sources along with a discussion of the challenges faced when generating range corrections for single frequency geo stationary satellites. The analysis shows that the GEO provides a substantial improvement in WAAS availability in Alaska under nominal conditions and significant improvements everywhere in North America when GPS satellites are unavailable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".