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Record W2034357467 · doi:10.5194/angeo-21-1839-2003

Turbulence characteristics inside ionospheric small-scale expanding structures observed with SuperDARN HF radars

2003· article· en· W2034357467 on OpenAlexfundno aff
R. André, C. Hanuise, J. P. Villain, V. Krasnoselskikh

Bibliographic record

VenueAnnales Geophysicae · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsIonospherePhysicsGeophysicsTurbulenceScatteringComputational physicsPlasmaIonospheric heaterIncoherent scatterMeteorologyOptics

Abstract

fetched live from OpenAlex

Abstract. Unusual structures characterized by a very high-velocity divergence have been observed in the high-latitude F-region with SuperDARN radars (André et al., 2000). These structures have been interpreted as due to local demagnetization of the plasma in the ionospheric F-region, during very specific geophysical conditions. In this study, the collective wave scattering theory is used to characterize the decameter-scale turbulence (l approx 15 m) inside the structures. The distribution function of the diffusion coefficient is modified when the structures are generated, suggesting that two regimes of turbulence coexist. A temporal analysis decorrelates the two regimes and gives access to the dynamics associated with the structures. It is shown that a high turbulent regime precedes the plasma demagnetization and should be related to an energy deposition. Then a second regime appears when the plasma is demagnetized and disappears simultaneously with the structures. This study is the first application of the collective wave scattering theory to a specific geophysical event.Key words. Ionosphere (auroral ionosphere; ionospheric irregularities) – Space plasma physics (turbulence)

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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.

Study designObservational
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

Citations3
Published2003
Admission routes1
Has abstractyes

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