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.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".