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Record W1956964248 · doi:10.1002/jgra.50531

Simulation of O<sup><b>+</b></sup> upflows created by electron precipitation and Alfvén waves in the ionosphere

2013· article· en· W1956964248 on OpenAlexaff
D. Sydorenko, R. Rankin

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectron precipitationIonospherePhysicsIonPonderomotive forceElectric fieldElectronAtomic physicsMagnetosphereEarth's magnetic fieldStanding waveThermosphereGeophysicsComputational physicsMagnetic fieldPlasmaOpticsNuclear physics

Abstract

fetched live from OpenAlex

A two‐dimensional model of magnetosphere‐ionosphere coupling is presented. It includes Alfvén wave dynamics, ion motion along the geomagnetic field, chemical reactions between ions and neutrals, collisions between different species, and a parametric model of electron precipitation. Representative simulations are presented, along with a discussion of the physical mechanisms that are important in forming oxygen ion field‐aligned plasma flows. In particular, it is demonstrated that ion upwelling is strongly affected by the ponderomotive force of standing Alfvén waves in the ionospheric Alfvén resonator, and by enhanced electric fields that are produced when electrons are heated by soft electron precipitation. It is verified that the simulations are in qualitative agreement with available theoretical predictions. In the resonator, in addition to the ponderomotive force, a contribution to the upflow comes from centrifugal acceleration. Heating by the current of standing waves increases parallel electric fields and ion pressure gradients only at low altitudes where they are easily balanced by friction with neutrals. This prevents development of fast field‐aligned ion flows in the E ‐layer and lower F ‐layer.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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 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

Citations13
Published2013
Admission routes1
Has abstractyes

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