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Record W4212982410 · doi:10.1038/s41467-022-28521-3

Observational evidence of ring current in the magnetosphere of Mercury

2022· article· en· W4212982410 on OpenAlexaff
Jiutong Zhao, Qiugang Zong, Chuan Yue, W. Sun, Hui Zhang, X.-Z. Zhou, G. Le, R. Rankin, J. A. Slavin, J. M. Raines, Ying Liu, Yong Wei

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsRing currentMagnetosphereMagnetopausePhysicsMercury's magnetic fieldDipolePlanetMercury (programming language)Magnetic fieldCurrent sheetMagnetic dipoleGeophysicsComputational physicsAstrobiologyAstrophysicsEarth's magnetic fieldL-shellMagnetohydrodynamics

Abstract

fetched live from OpenAlex

The magnetic gradient and curvature drift of energetic ions can form a longitudinal electric current around a planet known as the ring current, that has been observed in the intrinsic magnetospheres of Earth, Jupiter, and Saturn. However, there is still a lack of observational evidence of ring current in Mercury's magnetosphere, which has a significantly weaker dipole magnetic field. Under such conditions, charged particles are thought to be efficiently lost through magnetopause shadowing and/or directly impact the planetary surface. Here, we present the observational evidence of Mercury's ring current by analysing particle measurements from MErcury Surface, Space Environment, GEochemistry, and Ranging (MESSENGER) spacecraft. The ring current is bifurcated because of the dayside off-equatorial magnetic minima. Test-particle simulation with Mercury's dynamic magnetospheric magnetic field model (KT17 model) validates this morphology. The ring current energy exceeds [Formula: see text] J during active times, indicating that magnetic storms may also occur on Mercury.

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)0.000
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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