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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".