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Record W1539758185 · doi:10.1002/2015gl064700

Using patchy pulsating aurora to remote sense magnetospheric convection

2015· article· en· W1539758185 on OpenAlexafffund
E. Donovan, Jun Liang, J. M. Ruohoniemi, E. Spanswick

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNational Aeronautics and Space AdministrationCanadian Space AgencyNational Science Foundation
KeywordsConvectionPhysicsGeophysicsConvection cellGeologyRadarAzimuthMagnetosphereMechanicsPlasmaCombined forced and natural convectionAstronomyComputer scienceNatural convection

Abstract

fetched live from OpenAlex

Abstract Five patchy pulsating aurora (PPA) patches have been identified in data obtained from the Gillam Time History of Events and Macroscale Interactions during Substorms (THEMIS) all‐sky imager (ASI). We found that azimuthal velocities of five patches derived from THEMIS ASI data were close to the same value as the local convection velocities as obtained from analysis of Super Dual Auroral Radar Network data, consistent with the idea that the patch motion is primarily due to E × B convection. We argue that this means that we can infer 2‐D maps of the time‐evolving convection from time sequences of PPA. Further, given that the E × B convection is understood to be a projection of magnetospheric convection, this means that, provided auroral and viewing conditions cooperate, patch motion can be used for remote sensing magnetospheric convection over across‐extended regions with fairly high time resolution. This is the first detailed demonstration of the equivalence of patch velocities and E × B convection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.065
GPT teacher head0.342
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations35
Published2015
Admission routes2
Has abstractyes

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