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Record W3154678648 · doi:10.1029/2020ja029057

Empirical Loss Timescales of Slot Region Electrons due to Plasmaspheric Hiss Based on Van Allen Probes Observations

2021· article· en· W3154678648 on OpenAlexaff
Qi Zhu, Xing Cao, Xudong Gu, Binbin Ni, Zheng Xiang, Song Fu, Danny Summers, Man Hua, Yuequn Lou, Xin Ma, Yingjie Guo, Deyu Guo, Wenxun Zhang

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMemorial University of Newfoundland
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsHissSubstormPlasmaspherePhysicsElectronVan Allen ProbesScatteringComputational physicsPitch angleVan Allen radiation beltAtomic physicsGeophysicsNuclear physicsPlasmaMagnetosphereOptics

Abstract

fetched live from OpenAlex

Abstract Based on Van Allen Probes observations, in this study we perform a statistical analysis of the spectral intensities of plasmaspheric hiss at L‐shells of 1.8–3.0 in the slot region. Our results show that slot region hiss power intensifies with a strong day‐night asymmetry as the level of substorm activity or L‐shell increases. Using the statistical spectral profiles of plasmaspheric hiss, we calculate the drift‐ and bounce‐averaged electron pitch angle diffusion coefficients and subsequently obtain the resultant electron loss timescales through 1‐D Fokker‐Planck simulations. We find that slot region electron loss timescales vary significantly from <1 day to several years, showing a strong dependence on electron energy, L‐shell and substorm activity. We also construct an empirical model of slot region electron loss timescales due to scattering by plasmaspheric hiss, which agrees well with the 1‐D simulation results and can be readily used in modeling the dynamics of slot region electrons.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.334
Teacher spread0.285 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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