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Record W4398205071 · doi:10.1029/2024gl109000

Red Line Diffuse‐Like Aurora Driven by Time Domain Structures Associated With Braking Magnetotail Flow Bursts

2024· article· en· W4398205071 on OpenAlexaff
Yangyang Shen, Jun Liang, Anton Artemyev, V. Angelopoulos, Qianli Ma, L. R. Lyons, Jiang Liu, Y. Nishimura, Xiao‐Jia Zhang, I. Y. Vasko, D. L. Hampton

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersDivision of Atmospheric and Geospace SciencesNational Aeronautics and Space Administration
KeywordsLine (geometry)Flow (mathematics)PhysicsTime domainGeophysicsGeodesyComputer scienceGeologyMechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract Magnetotail earthward‐propagating fast plasma flows provide important pathways for magnetosphere‐ionosphere coupling. This study reexamines a flow‐related red‐line diffuse‐like aurora event previously reported by Liang et al. (2011, https://doi.org/10.1029/2010ja015867 ), utilizing THEMIS and ground‐based auroral observations from Poker Flat. We find that time domain structures (TDSs) within the flow bursts efficiently drive electron precipitation below a few keV, aligning with predominantly red‐line auroral intensifications in this non‐substorm event. The diffuse‐like auroras sometimes coexisted with or potentially evolved from discrete forms. We forward model red‐line diffuse auroras due to TDS‐driven precipitation, employing the time‐dependent TREx‐ATM auroral transport code. The good correlation (∼0.77) between our modeled and observed red line emissions underscores that TDSs are a primary driver of the red‐line diffuse‐like auroras, though whistler‐mode wave contributions are needed to fully explain the most intense red‐line emissions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.255
Teacher spread0.247 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research Letters→Same topicIonosphere and magnetosphere dynamics→French-language works237,207→