Global and Regional Ionospheric Corrections for Faster PPP Convergence
Bibliographic record
Abstract
Rapid convergence of precise point positioning (PPP) solutions to cm-level precision is a key factor for many applications. One means of accelerating this convergence is to exploit the benefit of information on the ionosphere. In order to preserve the integer nature of carrier-phase ambiguities in PPP, it is imperative that ionospheric corrections be provided with a set of compatible satellite phase biases. When using the decoupled-clock model, global ionospheric maps (GIMs) currently provided by the International GNSS Service are not directly applicable to PPP with ambiguity resolution. This paper describes a methodology for incorporating external ionospheric corrections into this model. It is shown that the use of both GIMs and ambiguity resolution can potentially reduce the convergence time of PPP to 10-cm horizontal accuracies from 30 to 4.5 minutes (68th percentile), while a regional network with inter-station spacing of 150 km can reach this threshold instantaneously under favorable ionospheric conditions. © 2014 Her Majesty the Queen in Right of Canada. NAVIGATION. © 2014 the Institute of Navigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 0.003 |
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".