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Record W2044949058 · doi:10.1002/2013ja019217

Ionospheric feedback instability and active discrete auroral forms

2014· article· en· W2044949058 on OpenAlexaboutno aff
Nan Jia, A. V. Streltsov

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSubstormIonosphereGeophysicsMagnetospherePhysicsInstabilityElectric fieldMagnetic fieldPlasmaComputational physicsEarth's magnetic fieldMechanics

Abstract

fetched live from OpenAlex

Abstract We present results from time‐dependent, three‐dimensional numerical simulations of ULF Alfvén waves generated by the ionospheric feedback instability at high latitudes. The goal of this study is to understand physical mechanisms responsible for the formation of active discrete auroral forms (curls and folds) typically observed during the substorm onset. Our simulations demonstrate that active feedback of the auroral ionosphere on magnetic field‐aligned currents carried by ULF Alfvén waves can explain wide variety of auroral structures. The main reason for this variety is a susceptibility of the electrodynamics of the coupled magnetosphere‐ionosphere system to parameters of the electric field and plasma in the ionosphere and magnetosphere of the Earth. Due to the highly nonlinear character of the magnetosphere‐ionosphere interactions, different combinations of these parameters lead to different spatial and temporal behavior of the magnetic field‐aligned currents producing aurora. One of the main conclusions from our study is that the ionosphere is responsible for the formation of small‐scale curls and folds in the discrete aurora and for the intensification of magnetic field‐aligned currents, observed during substorms. The results from our 3‐D simulations are applied for the explanation of structure of discrete auroral arcs observed during the 29 October 2013 substorm at Fort Yukon, Alaska.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.303
Teacher spread0.287 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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