PPP using NRCan Ultra Rapid products (EMU): Near real-time comparison and monitoring of time scales generated in time and frequency laboratories
Bibliographic record
Abstract
The Precise Point Positioning (PPP) time-transfer technique requires the availability of precise estimates of GNSS satellite orbits and clock offsets. Several such products are available from the International GNSS Service (IGS), each having their own characteristics: robustness, update rate, latency and satellite clock offset time interval. The most frequently updated IGS products are the Ultra Rapid products, which are generated four times a day with a latency of three hours. Natural Resources Canada (NRCan) contributes its own Ultra Rapid GPS product to the IGS for combination. However, the underlying processes running at NRCan generate products much more frequently - 24 times a day - with a latency of 90 minutes, offering an opportunity for more timely time-transfer results when used in PPP. INRIM and NRCan hereby assess the potential of using the PPP with the NRCan Ultra Rapid GPS products to serve as a short latency time-transfer tool. A specific experiment has been set up, where the NRCan Ultra Rapid GPS products, as well as all currently available IGS products, are used in PPP time transfer between selected IGS stations collocated in timing laboratories. Results and relative merits are compared in light of their respective delivery and frequency stability characteristic, in view of designing an automated near real-time monitoring system to assist timing laboratories in operational maintenance of frequency standards and time scales dissemination to external users.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".