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Record W2079595503 · doi:10.1186/bf03352825

Effects of latitudinal distributions of particle density and wave power on cyclotron resonant diffusion rates of radiation belt electrons

2008· article· en· W2079595503 on OpenAlexafffund
Danny Summers, Binbin Ni

Bibliographic record

VenueEarth Planets and Space · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVan Allen radiation beltHissPhysicsPitch angleComputational physicsElectronWave powerCyclotron resonanceVan Allen ProbesElectron precipitationDiffusionAtomic physicsElectron densityCyclotronGeophysicsMagnetosphereMagnetic fieldPower (physics)Nuclear physics

Abstract

fetched live from OpenAlex

Abstract We evaluate cyclotron resonant interactions of radiation belt electrons with VLF chorus, plasmaspheric ELF hiss and electromagnetic ion cyclotron (EMIC) waves. We assume that the Earth’s magnetic field is dipolar and that each wave mode has a Gaussian spectral density. The dependence of the resonant electron diffusion rates on the latitudinal distributions of particle density and wave power is examined. We find that while the diffusion rates can be sensitive to the latitudinal distributions of density and wave power, in general the sensitivity depends on wave mode, equatorial pitch-angle, electron energy and L-shell. We determine the effects of the latitudinal distributions of density and wave power on the electron precipitation loss timescale due to combined scattering by VLF chorus, ELF hiss and EMIC waves. Accurate modeling of radiation belt electron dynamics requires observational data on the global distributions of particle number density and wave power.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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 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

Citations40
Published2008
Admission routes2
Has abstractyes

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