Comparison between four integer ambiguity resolved PPP GNSS time transfer software solutions
Bibliographic record
Abstract
Abstract We present a comparison between four different software solutions (dubbed PPP-AR or IPPP, all based on the resolution of integer ambiguities of the carrier-phase) that compute time links between GNSS receivers. Additional processing layers have been specially developed to enable usage for time transfer purposes. A variety of GNSS receivers connected to UTC(k) timescales across the globe, covering a wide range of baselines and several GNSS receiver models was used in this work. For one of the links, the availability of an optical fiber link between the stations allowed a comparison of each software-based GNSS link to this common reference, otherwise links were compared with each other using a specially-developed four-cornered hat algorithm. In the performance analysis we focused on the frequency stability of the time links. Results show that all four independently developed software solutions agree within 20 ps on TDEV for all averaging times and highlight the importance of mitigating day-boundary phase discontinuities. This demonstrates the reliability of the different implementations of the PPP-AR/IPPP technique for operational time transfer.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".