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Record W4399051950 · doi:10.1029/2024ja032544

Rapid Acceleration Bursts in the Van Allen Radiation Belt

2024· article· en· W4399051950 on OpenAlexafffund
L. Olifer, Steven K. Morley, L. G. Ozeke, I. R. Mann, Milla Kalliokoski, M. G. Henderson, Matthew Carver, A. Hoover

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsVan Allen radiation beltAccelerationVan Allen ProbesPhysicsSpacecraftRadiationSatelliteElectronMagnitude (astronomy)Orbit (dynamics)Computational physicsGeodesyAstrophysicsAstronomyAerospace engineeringMagnetosphereMagnetic fieldGeologyClassical mechanicsOpticsNuclear physics

Abstract

fetched live from OpenAlex

Abstract The fast Van Allen radiation belt electron dynamics during geomagnetic storms have not yet been fully explained, in part due to limitations of standard satellite missions in both orbit and the number of spacecraft. Here we overcome these limitations using measurements from the Global Positioning System (GPS) constellation during an acceleration event on 26 August 2018. We show that the acceleration of relativistic electrons occurs in two distinct bursts, each dominated by a different acceleration mechanism. The first burst enhances the radiation belt electrons by four orders of magnitude in 2 hr and is consistent with ULF‐wave radial diffusion. The second burst is likely caused by the local acceleration and delivers an order‐of‐magnitude increase in 20 min. This work demonstrates how distributed, operational measurements can be used to resolve phenomena not observable with previous capabilities, and that rapid energization of the radiation belt can occur much faster than previously reported.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.340
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2024
Admission routes2
Has abstractyes

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