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Record W2054361264 · doi:10.5194/angeo-28-1345-2010

The effects of mesoscale regions of precipitation on the ionospheric dynamics, electrodynamics and electron density in the presence of strong ambient electric fields

2010· article· en· W2054361264 on OpenAlexafffund
John D. de Boer, Jean-Marc Noël

Bibliographic record

VenueAnnales Geophysicae · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of SaskatchewanRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsIonosphereAdvectionElectron precipitationMesoscale meteorologyElectric fieldElectronConvectionPrecipitationGeophysicsElectron densityIonizationComputational physicsCurrent densityAtmospheric sciencesPlasmaIonMagnetosphereMechanicsMeteorology

Abstract

fetched live from OpenAlex

Abstract. We have developed a new high resolution two-dimensional model of the high latitude ionosphere in which nonlinear advection terms are closely coupled with the electrodynamics. The model provides a self-consistent description of the ionospheric feedback on the electrodynamical perturbations produced by auroral arc-related particle precipitation in regions with strong ambient electric fields. We find in particular that a heretofore neglected ion Pedersen advection term can introduce considerable changes in the electron density profile, the current density distribution, the conductivities and the electron temperatures. We find that the convective effects can carry the ionisation more than 150 km outside the precipitation region in a few minutes, with attendant large changes in the current distribution and E-region densities that become enhanced outside the region of particle precipitation. The production of a tongue of ionisation that slowly decays outside the auroral boundaries contrasts with the sharp geometric cut-off and associated stronger current densities found in previous studies.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.370

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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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