Dependence of GLONASS Pseudorange Inter-frequency Bias on Receiver-Antenna Combination and Impact on Precise Point Positioning
Bibliographic record
Abstract
GLONASS pseudorange observations are affected by inter-frequency channel biases (ICBs) due to the frequency division multiple access (FDMA) satellite signal structure. This research estimated the GLONASS pseudorange inter-frequency channel biases using 350 IGS stations, based on 32 receiver types and over 11 antenna types over a period of 1 week, DOY 195 to 201 in 2013. An improvement of 19% and 1% was observed after calibrating out the pseudorange ICBs, in the horizontal and vertical components, respectively, considering a 20-min convergence period. Two major contributions are presented. The first contribution is the presentation of the four different scenarios involving varying different receiver and antenna types and how that variability affects the characteristics of ICBs. Attention is also drawn to the characteristics of the Analysis Center (AC) satellite common mean errors. In relation to the antipodal nature of the GLONASS satellites, the correlation of the GLONASS frequency numbers with the AC-satellite common mean errors is addressed. Copyright © 2016 Institute of Navigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".