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Record W4280628596 · doi:10.1029/2022gl097978

Nonlinear Wave Growth Analysis of Chorus Emissions Modulated by ULF Waves

2022· article· en· W4280628596 on OpenAlexaff
Li Li, Yoshiharu Omura, Xu‐Zhi Zhou, Qiugang Zong, R. Rankin, Chao Yue, S. Y. Fu

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science Foundation
KeywordsChorusPhysicsSubstormMagnetosphereAmplitudeLongitudinal waveNonlinear systemGeophysicsComputational physicsMagnetic fieldWave propagationOptics

Abstract

fetched live from OpenAlex

Abstract We present Van Allen Probes observations of periodic chorus wave emissions in the troughs of compressional ultralow frequency (ULF) waves. During this event, the spectral gap of chorus waves gradually widens as the spacecraft moves from the equatorial source region towards higher latitudes. Moreover, chorus wave intensity increases and frequency range widens after a substorm injection. We show that the periodic occurrence of chorus waves is attributed to the modulation of threshold amplitude for nonlinear growth of chorus waves by the second spatial derivative of ULF compressional magnetic field. The widening gap can be interpreted in terms of the nonlinear damping mechanism. A good agreement is also found between the nonlinear wave growth theory and the observations regarding the influence of substorm injection on the chorus. These findings support the applicability of the nonlinear theory in describing the chorus wave generation and damping, together with their modulations by ULF waves.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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 head0.015
GPT teacher head0.280
Teacher spread0.264 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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