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Record W1770103121 · doi:10.1002/2014gl060787

Modeling cross L shell impacts of magnetopause shadowing and ULF wave radial diffusion in the Van Allen belts

2014· article· en· W1770103121 on OpenAlexafffund
L. G. Ozeke, I. R. Mann, D. L. Turner, K. R. Murphy, Alex Degeling, I. J. Rae, D. K. Milling

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersScience and Technology Facilities CouncilEuropean CommissionNatural Sciences and Engineering Research Council of CanadaNatural Environment Research CouncilAlberta InnovatesFP7 SpaceNational Aeronautics and Space Administration
KeywordsVan Allen radiation beltMagnetopausePlasmasphereVan Allen ProbesPhysicsGeophysicsMagnetosphereComputational physicsGeomagnetic stormSpace weatherFlux (metallurgy)DiffusionGeomagnetically induced currentSolar windPlasma

Abstract

fetched live from OpenAlex

Abstract We present simulations of the outer electron radiation belt using a new ULF wave‐driven radial diffusion model, including empirical representations of loss due to chorus and plasmaspheric hiss. With an outer boundary condition constrained by in situ electron flux observations, we focus on the impacts of magnetopause shadowing and outward radial diffusion in the heart of the radiation belt. Third invariant conserving solutions are combined to simulate the L shell and time dependence of the differential flux at a fixed energy. Results for the geomagnetically quiet year of 2008 demonstrate not only remarkable cross L shell impacts from magnetopause shadowing but also excellent agreement with the in situ observations even though no internal acceleration source is included in the model. Our model demonstrates powerful utility for capturing the cross‐L impacts of magnetopause shadowing with significant prospects for improved space weather forecasting. The potential role of the plasmasphere in creating a third belt is also discussed.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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 designSimulation or modeling
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

Citations36
Published2014
Admission routes2
Has abstractyes

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