Analysis of assistance data on AGPS performance
Bibliographic record
Abstract
The integration of GPS into mobile telephones enables a potentially vast array of new applications ranging from consumer products to safety of life and security applications. In the United States, Enhanced-911 regulations have been a major catalyst for this deployment while in Europe, the commercial potential of location-based services is driving it. However, these new applications as well as the mobile phone environment itself pose significant GPS challenges. These include low cost implementation in restricted spaces on the mobile phone unit and reliable operation in a broad range of environments. These challenges and the availability of mobile communication itself spawned the concept of assisted GPS (AGPS) in which the network assists the receiver to perform various functions. This paper reports on the fundamental signal acquisition and tracking capability of an AGPS receiver under weak signal conditions as well as the impact of different types of aiding acquisition and tracking performance. A SiRFLoc™ evaluation kit is used to investigate performance. Tests are conducted using a hardware simulator and results are analysed in terms of time-to-first-fix (TTFF) and position accuracy. It is found that an AGPS receiver provides a 13 dB improvement in acquisition sensitivity over a comparable high sensitivity receiver operating in unaided mode. The accuracy of timing, the initial reference position and the associated uncertainty of the initial position all have an impact on the TTFF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".