Precise Point Positioning technique for short and long baselines time transfer
Bibliographic record
Abstract
In this work the clock parameters determination of several timing receivers TTS-4 (AOS, PL), ASHTECH-Z-XII3T (PTB, USNO) and SEPTENTRIO POLARX4TR (ORB,) by use of the Precise Point Positioning (PPP) technique were presented. The clock parameters were determined for several time links based on the data delivered by time and frequency laboratories mentioned above. The computations cover the period from June 1 2012 to May 31, 2013 and were performed in two modes with 7-day and one-month solution for all links. All RINEX data files which include phase and code GPS data were recorded in 30-second intervals. All calculations were performed by means of Natural Resource Canada's GPS Precise Point Positioning (GPS-PPP) software based on high-quality precise satellite coordinates and satellite clock delivered by IGS as the final products. The received results were compared with the results obtained by GPS CV and TWSTFT. The used independent PPP technique is a very powerful and simple method which allows for better control of antenna positions in AOS and a verification of other time transfer techniques like GPS CV, GLONASS CV and TWSTFT. The precision of the PPP technique is only comparable to the glass fiber link PL-AOS realized at present by PL and AOS time labs. Currently PPP technique is one of the main time transfer methods used at AOS considerably improving and strengthening the quality of the Polish time scales UTC(AOS), UTC(PL), and TA(PL).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.006 | 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".