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Record W2758554686 · doi:10.1029/2019ja027067

Drift‐Bounce Resonance Between Charged Particles and Ultralow Frequency Waves: Theory and Observations

2019· article· en· W2758554686 on OpenAlexaff
Yifan Zhu, Shujie Gu, Xu‐Zhi Zhou, Qiugang Zong, Jie Ren, Xin‐Ran Sun, Ying Liu, Shuai Zhang, Quanqi Shi, R. Rankin

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPhysicsResonance (particle physics)Pitch angleCharged particleSpacecraftComputational physicsParticle (ecology)GeophysicsIonAtomic physicsGeologyAstronomy

Abstract

fetched live from OpenAlex

Abstract Ultralow frequency (ULF) waves have long been known to resonate with magnetospheric charged particles through their drift and bounce motions. Most research interest has focused on the resonance with drift motion, which can accelerate charged particles at very high energies. The role of the bounce motion, especially for particles with lower energies, has attracted less attention so far. Here we start from the general theory of wave‐particle interactions to predict the characteristic, observable signatures of drift‐bounce resonance. Such signatures can be described in the particle pitch angle spectrum as a series of inclined stripes, with the inclination angle depending on the latitude of the observing spacecraft. Each stripe is also twisted at two conjugated pitch angles, suggesting significant phase shifts across resonant pitch angles. These predicted signatures are found consistent with observations from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) spacecraft, and therefore provide an identification of drift‐bounce resonance together with a validated picture over the importance of particle's bounce motion in the ULF wave‐particle interactions.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.635

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 communication0.0000.001
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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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 designTheoretical or conceptual
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

Citations38
Published2019
Admission routes1
Has abstractyes

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