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Record W2363088680 · doi:10.1002/2016gl069230

A comparison between large‐scale irregularities and scintillations in the polar ionosphere

2016· article· en· W2363088680 on OpenAlexaff
Yanling Wang, Qinghe Zhang, P. T. Jayachandran, M. Lockwood, Shun‐Rong Zhang, J. Moen, Zan‐Yang Xing, Yu‐Zhang Ma, M. Lester

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNational Key Research and Development Program of ChinaNatural Environment Research CouncilNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaNational Aeronautics and Space AdministrationNorges ForskningsrådSight Research UKScience and Technology Facilities CouncilJohns Hopkins University
KeywordsTECIonosphereScintillationGeomagnetic stormEarth's magnetic fieldPolarGeologyTotal electron contentGeophysicsStormPolar capLatitudeTrough (economics)AmplitudeAtmospheric sciencesGeodesyPhysicsMagnetic fieldAstronomyOptics

Abstract

fetched live from OpenAlex

Abstract A comparison tool has been developed by mapping the global GPS total electron content (TEC) and large coverage of ionospheric scintillations together on the geomagnetic latitude/magnetic local time coordinates. Using this tool, a comparison between large‐scale ionospheric irregularities and scintillations is pursued during a geomagnetic storm. Irregularities, such as storm enhanced density, middle‐latitude trough, and polar cap patches, are clearly identified from the TEC maps. At the edges of these irregularities, clear scintillations appeared but their behaviors were different. Phase scintillations ( σ φ ) were almost always larger than amplitude scintillations ( S 4 ) at the edges of these irregularities, associated with bursty flows or flow reversals with large density gradients. An unexpected scintillation feature appeared inside the modeled auroral oval where S 4 were much larger than σ φ , most likely caused by particle precipitations around the exiting polar cap patches.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.336

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 head0.024
GPT teacher head0.319
Teacher spread0.295 · 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 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

Citations53
Published2016
Admission routes1
Has abstractyes

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