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Record W4408550973 · doi:10.3390/universe11030101

Influence of Solar Wind Driving and Geomagnetic Activity on the Variability of Sub-Relativistic Electrons in the Inner Magnetosphere

2025· article· en· W4408550973 on OpenAlexaff
Evangelia Christodoulou, Christos Katsavrias, Panayotis Kordakis, Ioannis A. Daglis

Bibliographic record

VenueUniverse · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsMagnetosphereEarth's magnetic fieldSolar windGeomagnetic stormElectronVan Allen radiation beltIonospheric dynamo regionMagnetosphere of JupiterQuantum electrodynamicsAstronomyAstrophysicsMagnetic fieldNuclear physicsPlasmaQuantum mechanics

Abstract

fetched live from OpenAlex

Motivated by the need for more accurate radiation environment modeling, this study focuses on identifying and analyzing the drivers behind the sub-relativistic electron flux variations in the inner magnetosphere. We utilize electron flux data between 1 and 500 keV from the Hope and MagEIS instruments on board the RBSP satellites, as well as from the FEEPS instruments on board the MMS spacecrafts, along with solar wind parameters and geomagnetic indices obtained from the OmniWeb2 and SuperMag data services. We calculate the correlation coefficients between these parameters and electron flux. Our analysis shows that substorm activity is a crucial driver of the source electron population (10–100 keV), while also showing that seed electrons (100–400 keV) are not purely driven by substorm events but also from enhanced convection/inward diffusion. By introducing time lags, we observed a delayed response of electron flux to changes in geospace conditions, and we identified specific time lag periods where the correlation is maximum. This work contributes to our broader understanding of the outer belt sub-relativistic electron dynamics and forms the basis for future research.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.306

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 head0.003
GPT teacher head0.188
Teacher spread0.186 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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