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Record W4395031360 · doi:10.1029/2023gl105617

Statistical Analysis of Whistler Precursors Upstream of Foreshock Transient Shocks: MMS Observations

2024· article· en· W4395031360 on OpenAlexaff
Mengmeng Wang, Terry Z. Liu, Hui Zhang, Kaijun Liu, Quanqi Shi, Ruilong Guo, Anmin Tian, W. Sun, Xiaochen Shen, Siyuan Wu, A. W. Degeling, Yan Wang, Shichen Bai, Zhonghua Yao, Wenya Li, Shuai Zhang, T. Pitkänen, Shutao Yao, Ji Liu, Kun Cheng, Xiao Ma, Yuqi Liu

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsWhistlerForeshockPhysicsGeophysicsComputational physicsSolar windShock (circulatory)Polarization (electrochemistry)Shock wavePlasmaAftershockMechanicsGeologySeismologyNuclear physics

Abstract

fetched live from OpenAlex

Abstract Using the high‐time‐resolution data from the Magnetospheric Multiscale mission, precursor waves upstream of foreshock transient (FT) shocks are statistically investigated using the four‐spacecraft timing method. The wave frequencies and wave vectors determined in the plasma rest frame (PRF) are shown to follow the cold plasma dispersion relation for whistler waves. Combining with the feature of the right‐hand polarization in the PRF, the precursors are identified as whistler‐mode waves around the lower hybrid frequency. The occurrence of whistler precursors is independent of the Alfvén Mach number and the FT shock normal angle. More importantly, all the whistler precursors have group velocities pointing upstream in the shock frame, suggesting the dispersive radiation to be a possible generation mechanism. The study improves the understanding of not only the whistler precursors but also the overall FT shock 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 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

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