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Record W3026429241 · doi:10.1029/2020ja028119

Observational Evidence for the Role of Hall Conductance in Alfvén Wave Reflection

2020· article· en· W3026429241 on OpenAlexaff
Magnus F. Ivarsen, Jaeheung Park, Young‐Sil Kwak, Yaqi Jin, D. J. Knudsen, L. B. N. Clausen

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsIonosphereMagnetosphereGeophysicsReflection (computer programming)Computational physicsHall effectAlfvén wavePlasmaMagnetohydrodynamicsMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Electromagnetic energy carried by magnetohydrodynamic modes is an important mechanism in the energy transfer between the magnetosphere and the ionosphere. Alfvén waves are known to carry field‐aligned currents and thus play an important role in the dynamics of the ionosphere‐magnetosphere coupling. The role of Hall conductance in this interplay has been explored in magnetohydrodynamic models of the ionosphere but has hitherto not been observed in situ. We use 5 years of observations from the Swarm mission to shed light on this interplay. We present a high‐latitude climatology of both the measured Poynting flux and the measured Alfvén wave reflection coefficient. Our results indicate that high‐energy precipitation, which penetrates deep into the ionosphere and directly leads to strongly enhanced Hall conductance, is an important cause of positively interfering Alfvén wave reflection. We present such observational evidence and, with that, suggest that Hall conductance is substantially more important in the ionospheric wave reflection climatology than hitherto believed.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.216
GPT teacher head0.402
Teacher spread0.186 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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