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Record W2077370038 · doi:10.1002/2014ja020050

Auroral fragmentation into patches

2014· article· en· W2077370038 on OpenAlexaffabout
K. Shiokawa, Ayumi Hashimoto, Tomoaki Hori, K. Sakaguchi, Yasunobu Ogawa, E. Donovan, E. Spanswick, Martin Connors, Yuichi Otsuka, Shin‐ichiro Oyama, Satonori Nozawa, K. A. McWilliams

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsAthabasca UniversityUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsFragmentation (computing)GeophysicsIonosphereMagnetospherePhysicsInstabilityEarth's magnetic fieldPlasmaGeologyGeodesyMagnetic fieldMechanics

Abstract

fetched live from OpenAlex

Abstract Auroral patches in diffuse auroras are very common features in the postmidnight local time. However, the processes that produce auroral patches are not yet well understood. In this paper we present two examples of auroral fragmentation which is the process by which uniform aurora is broken into several fragments to form auroral patches. These examples were observed at Athabasca, Canada (geomagnetic latitude: 61.7°N), and Tromsø, Norway (67.1°N). Captured in sequences of images, the auroral fragmentation occurs as finger‐like structures developing latitudinally with horizontal‐scale sizes of 40–100 km at ionospheric altitudes. The structures tend to develop in a north‐south direction with speeds of 150–420 m/s without any shearing motion, suggesting that pressure‐driven instability in the balance between the earthward magnetic‐tension force and the tailward pressure gradient force in the magnetosphere is the main driving force of the auroral fragmentation. Therefore, these observations indicate that auroral fragmentation associated with pressure‐driven instability is a process that creates auroral patches. The observed slow eastward drift of aurora during the auroral fragmentation suggests that fragmentation occurs in low‐energy ambient plasma.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.326
Teacher spread0.307 · 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

Citations19
Published2014
Admission routes2
Has abstractyes

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