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Record W1894648890 · doi:10.1109/plasma.2002.1030438

Dispersive Alfven waves: nonlinear and kinetic effects

2003· article· en· W1894648890 on OpenAlexaff
R. Rankin, J. C. Samson, V. T. Tikhonchuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAlfvén wavePhysicsField lineElectronEarth's magnetic fieldComputational physicsElectron precipitationMagnetosphereMagnetic fieldField (mathematics)Electric fieldIonosphereGeophysicsMagnetohydrodynamicsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

Summary form only given, as follows. The auroral accelerator contains localized field-aligned potential drops capable of energizing electrons to many keV. One possible explanation involves excitation of dispersive Alfven field line resonances (FLRs) that lead to inverted-V electron precipitation and density cavities depleted of current carrying electrons. We present results of two-fluid modeling of nightside FLRs on stretched geomagnetic field lines, and discuss the various saturation mechanisms affecting the scale and spatial structure of the excited waves. We classify various nonlinearities affecting the evolution of FLRs, and discuss the need for a kinetic treatment of parallel electron dynamics: The bounce time of electrons on geomagnetic field lines is much smaller than the wave period of observed mHz FLRs, implying that the electron dynamics is very non-local along the field line. Accounting for the mirror force, and solving the Vlasov equation for parallel electron motion, we show that in the auroral accelerator, the Alfven wave conductivity is much smaller than predicted by two-fluid theory. The large parallel Alfven wave current and low conductivity lead to very enhanced parallel electric fields, on the order of mV/m. Accounting for hot magnetospheric and cold ionospheric plasma populations, we show that the characteristic electron energy is comparable to the quasistatic potential associated with the density and temperature gradient along the field line, Finally, we compare the results of our model to observations, and indicate how auroral MPA data can be used to infer the stretching of nightside field lines that is necessary to explain the low frequencies of observed FLRs.

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)0.002
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: none
Teacher disagreement score0.093
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0930.019

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 head0.002
GPT teacher head0.194
Teacher spread0.191 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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