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Record W4225303478 · doi:10.1029/2022gl098249

Novel EMIC Wave Propagation Pathway Through Buchsbaum Resonance and Inter‐Hemispheric Wave Interference: Swarm Observations and Modeling

2022· article· en· W4225303478 on OpenAlexafffund
I. P. Pakhotin, I. R. Mann, Dmytro Sydorenko, R. Rankin

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersCanadian Space AgencyEuropean Space Agency
KeywordsEmic and eticPhysicsIonosphereVan Allen ProbesGeophysicsComputational physicsField lineMagnetosphereVan Allen radiation beltWave propagationOpticsMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract In situ conjugate electromagnetic ion cyclotron (EMIC) waves observed by the Swarm mission in both hemispheres are presented. A complex and unusual pattern of Alfvénic EMIC wave energy is observed, with a mid‐latitude peak close to the source at L =3.3, as well as a secondary lower L ‐peak. A wave propagation model reveals that the secondary peak at L =1.7 may be explained by wave power being redirected equatorward due to the Buchsbaum resonance, crossing and interfering with the same EMIC wave power propagating equatorwards from the opposite hemisphere. This interference creates a coherent equatorial driver for a low‐ L field line resonance at the secondary peak, and which is associated with strong shear‐to‐fast mode coupling in the ionosphere. This behavior complicates the interpretation of low‐Earth orbit EMIC data for applications assessing radiation belt loss. Combined low Earth orbit observations and modeling enable these novel and localized magnetosphere‐ionosphere EMIC wave propagation pathways to be identified.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

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 head0.065
GPT teacher head0.286
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations22
Published2022
Admission routes2
Has abstractyes

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