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Record W2805277319 · doi:10.1029/2018gl077898

Generation of Electron Acoustic Waves in the Topside Ionosphere From Coupling With Kinetic Alfven Waves: A New Electron Energization Mechanism

2018· article· en· W2805277319 on OpenAlexafffund
Run Shi, Binbin Ni, Danny Summers, Huixin Liu, Akimasa Yoshikawa, Beichen Zhang

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersFundamental Research Funds for the Central UniversitiesJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhysicsIonosphereElectronKinetic energyComputational physicsElectron densityAcoustic waveIon acoustic waveElectron temperatureAtomic physicsGeophysicsOpticsClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Results from a new drift kinetic model in the topside ionosphere capture the mode conversion from kinetic Alfven waves to electron acoustic waves. When the kinetic Alfven waves propagate into the transition region, where the electron density of ionospheric origin becomes comparable to that of magnetospheric origin, the steep temperature gradient leads to the mode conversion. The electron acoustic waves are short‐lived by dissipating their energy into the electron energization, thus revealing a new type of electron acceleration in the topside ionosphere.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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