Analysis of Factors Affecting Ionospheric Refraction Estimates Obtained From Smartphone GNSS
Bibliographic record
Abstract
With the ever expanding capabilities, supply, and demand for smartphone Global Navigation Satellite System (GNSS) technology, the potential for crowd-sourced corrections is rapidly expanding. A key example that has been explored is crowd-sourced ionospheric corrections. With current capabilities set to outperform existing global maps and models in less dense regions, increases in ionospheric refraction estimate quality are an important next step to improve publicly available corrections. Thus, this research explores the potential relationships between various parameters and the estimated ionospheric refraction error in smartphone GNSS. A total of 10 parameters are examined with results indicating high correlations between parameters such as elevation angle, total number of satellites, signal-to-noise ratio, and first frequency pseudorange post-fit residual. Additionally, other parameters such as second frequency pseudorange post-fit residuals and post-fit carrier-phase residuals on both frequencies were found to likely be uncorrelated, and conclusions indicate that they are not expected to be useful in estimating expectations of accuracy for smartphone ionospheric refraction estimates. Additionally, analysis on the performance of different smartphone models is presented. Ultimately, knowing and understanding these relationships and trends will allow an adaptive filtering approach to select the ideal filter duration to increase efficacy, improving the accuracy of individual estimates and the models they are used to generate.
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 | 0.000 | 0.000 |
| 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 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".