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Record W2109101275 · doi:10.5194/angeo-23-2249-2005

On the development and evolution of nonlinear ion acoustic wave packets

2005· article· en· W2109101275 on OpenAlexaff
A. M. Hamza

Bibliographic record

VenueAnnales Geophysicae · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIntermittencyPhysicsTurbulenceIonAmplitudeElectronIonosphereNonlinear systemWave packetComputational physicsMomentum (technical analysis)PlasmaAtomic physicsMechanicsQuantum mechanicsGeophysics

Abstract

fetched live from OpenAlex

Abstract. A simple model of ion fluctuations (ion acoustic and ion cyclotron fluctuations for example) driven by an electron current which leads to intermittent fluctuations when the linear growth rate exceeds the wave packet dispersion rate is analized. The normalized fluctuation amplitude eφ0/T can be much larger than the mass ratio (me/mi) level predicted by the conventional quasilinear theory or Manheimer's theory (see references in this document), and where φ0 represents the amplitude of the main peak of the ion fluctuations. Although the ion motion is linear, intermittency is produced by the strong nonlinear electron response, which causes the electron momentum input to the ion fluctuations to be spatially localized. We treat the 1-D case because it is especially simple from an intuitive and analytical point of view, but it is readily apparent and one can put forward the conjecture that the effect occurs in a three dimensional magnetized plasma. The 1-D analysis, as shown in this manuscript will clearly help identify the subtle difference between turbulence as conventionally understood and intermittency as it occurs in space and laboratory plasmas. Keywords. Meteorology and atmospheric dynamics (Turbulence) – Ionosphere (Wave-particles interactions) – Space plasma physics (Waves and instabilities)

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.200 · 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 designOther design
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

Citations2
Published2005
Admission routes1
Has abstractyes

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