31st Annual Precise Time and Time Interval (PTTI) MEeting ENHANCING GPS TIMING ENGINES USING WAAS SIGNALS
Bibliographic record
Abstract
Accurate timing sources are becoming a very important issue in the development of networked telecommunication systems. Since the early 1990’s, GPS has been exploited for this purpose. The common GPS time transfer technique is mostly used to minimize the timing error caused by satellite clock and ephemeris errors, and Selective Availability. This technique provides a typical timing accuracy of SO nanoseconds (I sigma). Currently, a new WAAS (Wide Area Augmentation System) is being developed under the authority of the FAA (Federal Aviation Adminisfration). This system is a SatelliteSased Augmentation System (SBAS) which will be used to enhance signal continuity, availability, and integrity to GPS receivers. WAAS is scheduled to be officially commissioned in the summer of 2000. This paper describes the features and performance of a GPS/WAAS Timing Engine developed by Marconi Canada. The paper will discuss the features of the WAAS system and how it can be used to dramatically decrease the timing errors of a GPS engine. Results obtained using a GPS simulator and live signals will be analyzed. Comparative results between a GPS only and a GPS/WAAS Timing Engine will be presented. Finally, additional features of the GPS/WAAS Timing Engine, such as TRAIM (Time Remote Autonomous Integrity Monitoring), will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.047 |
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".