Comparison of two continuous GPS carrier-phase time transfer techniques
Bibliographic record
Abstract
Global Positioning System (GPS) carrier-phase (CP) time transfer, as a widely accepted high-precision time transfer method, frequently shows a data-batch boundary discontinuity of up to 1 ns, because of the inconsistency of the phase ambiguities between two consecutive data batches. To eliminate the data-batch boundary discontinuity, several techniques have been proposed in recent years. The question is how large the solutions of these techniques differ from each other and how well the solutions are faithful to clocks. To answer these questions, this paper chooses two techniques to study: Revised RINEX-Shift (RRS) technique [1-2], and Phase Common-View (Phase-CV) technique [3-4]. This paper shows that the time deviation of the difference between the two techniques is below 100 ps, for an averaging time of less than 10 days. Especially, for an averaging time of less than 1 day, the time deviation is less than 30 ps. We also find that both RRS and Phase-CV match TWSTFT (two-way satellite time and frequency transfer) and TWOTFT (two-way optical-fiber time and frequency transfer) quite well. The difference is typically within ±0.3 ns for more than 20 days. The above results are all based on a short-distance links (less than 2500 km). A long-distance comparison between these two techniques, such as a transatlantic link, has not yet been investigated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".