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Record W2946448534 · doi:10.1029/2018gl081863

Statistical Properties of Hiss in Plasmaspheric Plumes and Associated Scattering Losses of Radiation Belt Electrons

2019· article· en· W2946448534 on OpenAlexafffund
Wenxun Zhang, Binbin Ni, H. Huang, Danny Summers, Song Fu, Zheng Xiang, Xudong Gu, Xing Cao, Yuequn Lou, Man Hua

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Science Foundation of Hubei ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsHissPlasmasphereVan Allen radiation beltVan Allen ProbesElectronPhysicsPlumeScatteringRadiationElectron precipitationAtomic physicsGeophysicsComputational physicsMagnetosphereMeteorologyNuclear physicsPlasmaOptics

Abstract

fetched live from OpenAlex

Abstract Whistler mode hiss acts as an important loss mechanism contributing to the radiation belt electron dynamics inside the plasmasphere and plasmaspheric plumes. Based on Van Allen Probes observations from September 2012 to December 2015, we conduct a detailed analysis of hiss properties in plasmaspheric plumes and illustrate that corresponding to the highest occurrence probability of plumes at L = 5.0–6.0 and MLT = 18–21, hiss emissions occur concurrently with a rate of >~80%. Plume hiss can efficiently scatter ~10‐ to 100‐keV electrons at rates up to ~10 −4 s −1 near the loss cone, and the resultant electron loss timescales vary largely with energy, that is, from less than an hour for tens of kiloelectron volt electrons to several days for hundreds of kiloelectron volt electrons and to >100 days for >5‐MeV electrons. These newly obtained statistical properties of plume hiss and associated electron scattering effects are useful to future modeling efforts of radiation belt electron dynamics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.617
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 teacher head, 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

Citations47
Published2019
Admission routes2
Has abstractyes

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