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Simulation of bounce resonance ULF wave-particle interactions

2016· article· en· W2536511857 on OpenAlexafffund
R. Rankin, C. Wang, Dmytro Sydorenko, Y. Wang, Qiugang Zong, X. Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsPhysicsComputational physicsResonance (particle physics)MagnetosphereElectronDispersion (optics)Atomic physicsDispersion relationAmplitudeDipoleWave–particle dualityIonospherePlasmaGeophysicsOpticsNuclear physics

Abstract

fetched live from OpenAlex

Poloidal mode ultra-low-frequency (ULF) waves with high azimuthal mode number (high-m) are common throughout Earth's magnetosphere. The electric fields in these waves are on the order of tens of millivolts per meter, and are large enough to energize ions and electrons via drift- and drift-bounce wave-particle resonance. In this article, we present full-orbit test-particle simulations of drift-bounce resonance using a numerical model of ULF waves. The model uses a dipole magnetic field and can accommodate a realistic ionosphere with height-resolved Pedersen and Hall conductivity. It is demonstrated that second-harmonic poloidal mode waves can energize ions to tens of keV depending on the wave amplitude. The test-particle simulations also predict ion flux oscillations that have energy dispersion similar to many satellite observations. It will be shown that features in the energy dispersion are in agreement with theoretical considerations.

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)0.001
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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