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Record W4416675437 · doi:10.1029/2025av001881

Redefining SAR Arc Generation: The Competing Roles of Magnetospheric and Ionospheric Energy Injection

2025· article· en· W4416675437 on OpenAlexaff
Jing Liu, Wenbin Wang, Jun Liang, Libo Liu, C. R. Martinis, J. Wroten, Yongliang Zhang, Yao Chen, Tru H. Cao, Yifan Lu

Bibliographic record

VenueAGU Advances · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsAirglowIonosphereEarth's magnetic fieldElectron precipitationArc (geometry)PlasmaFlux (metallurgy)MagnetosphereHeat flux

Abstract

fetched live from OpenAlex

Abstract Stable auroral red (SAR) arcs are luminous subauroral emissions produced by the collisional excitation of oxygen atoms during geomagnetically active times. While traditionally attributed to inner magnetospheric electron heating, recent observations and simulations challenge the exclusivity of this mechanism. Here, we resolve the ionospheric origin of SAR arcs using multi‐instrument observations and numerical simulations during the March 2015 geomagnetic storm. Both magnetospheric heat flux and ion‐neutral frictional heating, driven by subauroral plasma flows, independently generate SAR arcs with intensities surpassing background airglow by hundreds of Rayleighs. While thermal electron impact dominates red‐line emissions in both cases, the vertical structures diverge: frictional heating localizes emissions to altitudes of 250–400 km, whereas magnetospheric heating extends emissions above ∼280 km with broader altitudinal coverage. These results redefine SAR arc generation as a product of competing magnetospheric and ionospheric energy pathways, advancing our understanding of cross‐scale interactions in geospace.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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