An Assessment of Long-term Spatial Agnosticism of GNSS Positioning Degradation Risks Due to Ionospheric Conditions
Bibliographic record
Abstract
The Global Navigations Satellite Systems (GNSS) have been evolved into an essential infrastructure of modern civilisation, a public goods, and enabler of rapidly growing number of technology and socio-economic applications. However, GNSS applications often lack fundamental details on GNSS Positioning, Navigation, and Timing (PNT services performance to define and determine their Quality of Service (QoS). The lack of alignment with the core GNSS PNT deprives GNSS applications of assessing the risks of the GNSS PNT utilisation, thus leaving GNSS applications unable to prepare alternatives and mitigate the causes of GNSS PNT performance disruptions. Here we contributed to solution of the problem with the introduction and long-term performance assessment of the risk model of ionospheric-caused GNSS positioning degradation. Called the Probability of Occurrence (PoO), our team defined the risk model of GNSS positioning degradation caused by ionospheric conditions based on the long term observations of occurrences of degraded GNSS positioning performance. In the process of the GNSS risk model validation, the long-term PoO risk models are developed using the annual 2014 stationary GNSS horizontal positioning error observations derived from the GNSS pseudoranges collected at the International GNSS Service (IGS) reference stations situated in polar (Iqaluit, Canada) and sub-equatorial regions (Darwin, Australia). Two GNSS risk models are compared for similarity using statistical methods of Hausdorff distance and Cramér–von Mises statistical test. Research results show that two GNSS risk models are spatially agnostic, since no significant difference in two long-term GNSS risk models is found. The research results supports the conclusion of generality of the PoO GNSS risk model, and its ability to serve GNSS applications developers, operators, and users in determination of the QoS of particular GNSS applications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".