QUALITY ANALYSIS OF SMARTPHONE GNSS OBSERVATIONS AND IMPACT ON PRECISE POSITIONING
Bibliographic record
Abstract
Abstract. The use of low-cost and ultra-low-cost receivers such as smartphones and wearables has been raising these days due to their affordability and widespread availability. Along with the release of Android Version 7 in 2016, the raw GNSS measurements became accessible through the location API, which includes the GNSSMeasurement class and GNSSClock class. However, the users are required to extract the typical GNSS observations, such as pseudorange, carrier-phase, and Doppler observations, from the raw data coming from these two classes. It should be mentioned some quality concerns may arise during the conversion process from the raw GNSS measurements to the typical GNSS measurements. These quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this paper, we first analyse the quality of GNSS observations logged from smartphones. Furthermore, we address potential issues that can arise during the generation of GNSS observations. We also introduce our developed in-house software, UofC CSV2RINEX, which is designed to convert CSV files into RINEX files.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".