Wide-Area Grid-Based Slant Ionospheric Delay Corrections for Precise Point Positioning
Bibliographic record
Abstract
Introducing ionospheric information into a precise point positioning (PPP) solution enables faster ambiguity resolution and significantly improves positioning accuracy. To compute such corrections over wide areas, sparse networks with potentially irregular station distributions are often used. This aspect brings a new level of complexity as ionospheric corrections should be weighted appropriately in the PPP filter. This paper presents a possible implementation of grid-based wide-area slant ionospheric delay corrections, with a focus on the reported uncertainties. A balance is obtained between obtaining corrections with formal errors small enough to enable fast convergence, while large enough to overbound most errors. Based on least-squares collocation, the method uses satellite-specific variograms based on the 99th percentile values in each distance bin. Tested in southern Canada over a 53-week period in 2020, ionospheric grids allowed dual-frequency receivers to obtain around 5 cm accuracy in each horizontal component within 5 min of static data collection. For single-frequency solutions using data from geodetic receivers, positioning errors were reduced by over 60% for both static and kinematic processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".