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Record W4293795928 · doi:10.1029/2022gl100449

Ion Behavior at Shocklets: A Case Study of MMS Observations

2022· article· en· W4293795928 on OpenAlexaff
Zhi‐Yang Liu, Qiugang Zong, Hui Zhang, Jiutong Zhao, R. Rankin, C. J. Pollock, G. Le

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsWhistlerIonPhysicsForeshockSolar windGeophysicsCyclotronSpace physicsResonance (particle physics)MagnetosphereCyclotron resonancePlasmaAtomic physicsGeologyNuclear physicsSeismology

Abstract

fetched live from OpenAlex

Abstract The existence of shocklets, a kind of solitary structure, has been demonstrated in the Earth's foreshock regions for decades. Their formation and evolution are believed to be controlled by ions. However, the detailed behavior of ions at them has not been well investigated observationally yet. Here, we investigate a shocklet observed by the Magnetospheric Multiscale mission in the Earth's foreshock. Analysis of ion observations reveals that the solar wind (SW) ions are bunched in gyrophase space when interacting with the whistler precursor of the shocklet, suggesting the occurrence of cyclotron resonance between them. A more detailed examination suggests that the cyclotron resonance induces a net energy flow from the whistler precursor to the SW ions. Thus, the observations presented here indicate that the cyclotron resonance between shocklet whistler precursors and the SW ions could provide a mechanism for shocklet dissipation and SW ion energization.

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 score0.004
Threshold uncertainty score0.008

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.049
GPT teacher head0.324
Teacher spread0.275 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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