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Record W4412903601 · doi:10.1007/s10291-025-01939-0

Characterization and performance assessment of the GLONASS ionosphere model

2025· article· en· W4412903601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGPS Solutions · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersNatural Resources CanadaSmithsonian Astrophysical ObservatoryUniversity of New South Wales
KeywordsGLONASSIonosphereCharacterization (materials science)Remote sensingGNSS applicationsEnvironmental scienceGeologyComputer scienceAerospace engineeringGeodesyEngineeringGeophysicsPhysicsSatelliteOptics

Abstract

fetched live from OpenAlex

Abstract As part of the ongoing system modernization, the Russian navigation satellite system GLONASS has specified a dedicated electron density model supporting ionospheric path delay corrections for single-frequency navigation users. Solar-geophysical parameters for use with this model are made available through the code division multiple access (CDMA) signals, transmitted by selected GLONASS-K1, -K2 and -M+ satellites on the L3 and L1 frequencies. As a notable feature, the GLONASS ionosphere model can be used to predict the slant total electron content (STEC) through numerical integration of the 3-dimensional electron density along the signal path or a single-layer approximation of the 2-dimensional vertical total electron content (VTEC). Based on reference TEC values provided by global ionosphere maps, the performance of the GLONASS ionosphere model is assessed over an 11-year period using measured solar flux and geomagnetic activity values and compared with correction models of the GPS and Galileo constellations. Furthermore, the quality of solar-geophysical parameters made available in the CDMA navigation message over 1 year after launch of the first GLONASS-K2 satellite is evaluated. Compared to global ionosphere maps of the International GNSS Service, the GLONASS model exhibits VTEC biases in the range of roughly $${\pm 1}\,\textrm{TECU}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mspace/> <mml:mtext>TECU</mml:mtext> </mml:mrow> </mml:math> . Mean absolute errors (MAE) range from about $${5}\,\textrm{TECU}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mspace/> <mml:mtext>TECU</mml:mtext> </mml:mrow> </mml:math> in quiet years to 16 TECU at high solar activity. The corresponding mean absolute percentage errors (MAPE) range from roughly 50% (high activity) to 60% (low activity). Only minor performance differences were observed when comparing predictions based on broadcast values of solar flux and geomagnetic activity with observed values from space weather centers. On the other hand, a clear reduction of both the mean absolute (3–14 TECU) and mean absolute percentage errors (41–45%) is achieved when adjusting the adaptation coefficient of the GLONASS model based on the daily mean ratio of predicted and observed VTEC values. Irrespective of this, major VTEC modeling problems at very high solar activity could be identified. Overall, the GLONASS model outperforms the Klobuchar model but does not reach the prediction performance of the Galileo NeQuick-G and NTCM-G models, which exhibit errors of about 2–8 TECU (MAE) and 26–37% (MAPE).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it