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Record W4392457742 · doi:10.1029/2023gl107085

Localized Magnetopause Erosion at Geosynchronous Orbit by Reconnection

2024· article· en· W4392457742 on OpenAlexaff
Hyangpyo Kim, R. Nakamura, Hyunju Connor, Ying Zou, Ferdinand Plaschke, Niklas Grimmich, Brian M. Walsh, K. A. McWilliams, J. M. Ruohoniemi

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
FundersNational Aeronautics and Space Administration
KeywordsMagnetopauseGeosynchronous orbitMagnetic reconnectionGeophysicsOrbit (dynamics)PhysicsMagnetosphereMagnetosheathAstrobiologyEnvironmental scienceAstronomyAerospace engineeringPlasmaSatellite

Abstract

fetched live from OpenAlex

Abstract This study presents observations of magnetopause reconnection and erosion at geosynchronous orbit, utilizing in situ satellite measurements and remote sensing ground‐based instruments. During the main phase of a geomagnetic storm, Geostationary Operational Environmental Satellites (GOES) 15 was on the dawnside of the dayside magnetopause (10.6 MLT) and observed significant magnetopause erosion, while GOES 13, observing duskside (14.6 MLT), remained within the magnetosphere. Combined observations from the THEMIS satellites and Super Dual Auroral Radar Network radars verified that magnetopause erosion was primarily caused by reconnection. While various factors may contribute to asymmetric erosion, the observations suggest that the weak reconnection rate on the duskside can play a role in the formation of asymmetric magnetopause shape. This discrepancy in reconnection rate is associated with the presence of cold dense plasma on the duskside of the magnetosphere, which limits the reconnection rate by mass loading, resulting in more efficient magnetopause erosion on the dawnside.

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 score0.002
Threshold uncertainty score0.004

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 head0.013
GPT teacher head0.281
Teacher spread0.267 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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