Global Navigation Satellite Systems — Capabilities and Opportunities
Bibliographic record
Abstract
The capabilities of the current global navigation satellite system (GNSS), namely, the U.S. Global Positioning System (GPS), are first reviewed and the error sources affecting system performance are briefly described. Selected advances made recently to improve its accuracy, reliability, and availability are discussed and illustrated with examples. These include real-time kinematic (RTK) positioning, integration with self-contained inertial navigation systems (INS), attitude determination, and "indoor GPS". The latter, which deals with signal availability using weak signals, has resulted in intense research and development activities. It has also launched a rapid expansion of location-based services (LBS), a market expected to grow to over US $25B worldwide annually by the end of the decade. The characteristics and advantages of the second generation GPS, namely GPS II, that will become available throughout the decade ahead are summarized. Finally, the major characteristics and impact of Galileo, the European Union's GNSS, also to be deployed this decade, are discussed. The major conclusion is that GNSS multiplicity and performance improvements will have a major impact on the user community.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".