Modified ionosphere delay fitting model with atmosphere uncertainty grids for wide-area real-time positioning
Bibliographic record
Abstract
Precise atmospheric delay and proper constraints are critical for achieving rapid convergence and accurate positioning. However, ionospheric delay models over wide-area face challenges due to significant spatial and temporal variations, impacting real-time correction precision. To address this, we propose a novel ionospheric slant delay fitting model that adaptively selects the optimal reference path within coverage areas, describing differences between the reference propagation path and others through trigonometric functions. With ten coefficients, the model surpasses legacy polynomial fitting accuracy. Using a 166-station, 150 km-spaced European networks for atmospheric delays and 113 external stations for validation, our model achieves a 59.6% standard deviation reduction compared to the legacy model. Compared to the legacy ionospheric delay model, new model positioning convergence time (≤10 cm) accelerates by 37.7% and 34.2% for horizontal and vertical components, respectively. Meanwhile, two 2° × 2° uncertainty grids, generated from tropospheric and ionospheric delay fitting residuals at 15-min intervals, accurately describe fitting performance in all coverage areas with a maximum of 475 points. Adaptive constraints from uncertainty grids can reduce convergence time by 42.1% and 28.8% for horizontal and vertical, surpassing three-time modeling sigma solutions. These findings underscore the effectiveness of our novel ionospheric delay fitting model and the associated uncertainty grids in providing precise information across extensive regions with minimal coefficients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".