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Record W1957531677 · doi:10.1002/2015ja021026

High‐latitude thermospheric wind observations and simulations with SuperDARN data driven NCAR TIEGCM during the December 2006 magnetic storm

2015· article· en· W1957531677 on OpenAlexaboutno aff
Qian Wu, B. A. Emery, Simon Shepherd, J. M. Ruohoniemi, N. A. Frissell, J. L. Semeter

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsThermosphereGeomagnetic stormIonosphereAtmospheric sciencesStormEarth's magnetic fieldSolar windMiddle latitudesPolarEnvironmental scienceLatitudeConvectionMeteorologyClimatologyGeologyGeophysicsPhysicsGeodesyPlasmaMagnetic field

Abstract

fetched live from OpenAlex

Abstract Ion convection pattern derived from the Super Dual Auroral Radar Network potential pattern (SDP) data is developed for the National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamic General Circulation Model. The December 2006 geomagnetic storm event was simulated with the SDP ion convection pattern and two other existing input options (Heelis and Weimer convection models). The high‐latitude thermospheric wind simulated with SDP showed very good agreement with the Fabry‐Perot interferometer thermospheric wind data inside the polar cap at Resolute, Canada (74.7°N, 94.8°W, magnetic latitude 84). The Heelis model overestimated the winds during the storm event, because the model does not consider the cross polar cap potential saturation at high global geomagnetic index ( Kp ) values. The Weimer model provides a better performance than the Heelis model in this case. However, it has a larger discrepancy compared to the SDP results. The SDP provides an alternative data‐based tool for study of the ionosphere/thermosphere interactions in the polar cap.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.048
GPT teacher head0.305
Teacher spread0.257 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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