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Record W4311547842 · doi:10.1038/s41598-022-26189-9

Intense chorus waves are the cause of flux-limiting in the heart of the outer radiation belt

2022· article· en· W4311547842 on OpenAlexafffund
Suman Chakraborty, I. R. Mann, C. E. J. Watt, I. J. Rae, L. Olifer, L. G. Ozeke, J. K. Sandhu, B. H. Mauk, H. E. Spence

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaScience and Technology Facilities CouncilCanadian Space AgencyNatural Environment Research CouncilSight Research UK
KeywordsChorusVan Allen radiation beltLimitingFlux (metallurgy)GeophysicsRadiationPhysicsMedicineEngineeringMaterials scienceNuclear physicsMagnetosphere

Abstract

fetched live from OpenAlex

Chorus waves play a key role in outer Van Allen electron belt dynamics through cyclotron resonance. Here, we use Van Allen Probes data to reveal a new and distinct population of intense chorus waves excited in the heart of the radiation belt during the main phase of geomagnetic storms. The power of the waves is typically ~ 2-3 orders of magnitude greater than pre-storm levels, and are generated when fluxes of ~ 10-100 keV electrons approach or exceed the Kennel-Petschek limit. These intense chorus waves rapidly scatter electrons into the loss cone, capping the electron flux to a value close to the limit predicted by Kennel and Petschek over 50 years ago. Our results are crucial for understanding the limits to radiation belt fluxes, with accurate models likely requiring the inclusion of this chorus wave-driven flux-limiting process, that is independent of the acceleration mechanism or source responsible for enhancing the flux.

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 score0.002
Threshold uncertainty score0.005

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 studies0.0000.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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
Published2022
Admission routes2
Has abstractyes

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