Bibliographic record
Abstract
This special issue of the International Journal of Navigation and Observation deals with future global navigation satellite system (GNSS) signals. It is a timely issue in view of the current US GPS modernization efforts, the deployment of the EU’s Galileo, the replenishment of Russia’s GLONASS, and China’s plan to launch COMPASS. These systems, either individually or as a group, will provide tremendous availability, accuracy, and reliability enhancements to a consumer’s market that is growing at an annual doubledigit rate. Research is taking place not only to enhance the methods and algorithms to process the signals already in place but also to propose and optimize future signals and combinations thereof. The seven papers presented in this issue cover a variety of topics, ranging fromGalileo signal testing to signal multipath reduction, and represent a good cross-section of current activities in this area. A study of multipath performance of the initial Galileo signals transmitted by the GIOVEA satellite using actual data is described by Simsky et al., and a new generic approach called multiple gate delay tracking structures to reduce GNSS signal multipath is proposed and evaluated with different software approaches by Heikki Hurskainen et al. Also, Borio et al. discuss two strategies for the joint acquisition of data and pilot channels that are available on emerging signals. Shanmugam et al. present a short synchronization code design for future GNSS based on the optimization of specific performance criteria. Joint L/C-band code and carrier phase linear combination methods for Galileo are discussed by Henkel et al. Moreover, Lentmaier et al. discuss Bayesian time delay estimation based on particle filters for use in dynamic multipath environments. Finally, a comparison between Galileo CBOC candidates and BOC(1,1) signals in terms of detection performance is presented by Dovis et al.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.026 |
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".