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Record W2995048049 · doi:10.1029/2019gl085818

Magnetized Dust Clouds Penetrating the Terrestrial Bow Shock Detected by Multiple Spacecraft

2019· article· en· W2995048049 on OpenAlexaff
Hairong Lai, C. T. Russell, Y. D. Jia, Martin Connors

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPhysicsSolar windMagnetosheathInterplanetary magnetic fieldInterplanetary dust cloudBow shock (aerodynamics)Interplanetary spaceflightSpacecraftInterplanetary mediumAstrobiologyCoronal mass ejectionAstronomyMagnetic cloudMagnetopauseSolar SystemShock waveMagnetic fieldMechanics

Abstract

fetched live from OpenAlex

Abstract Clouds of charged nanometer dust, produced in intra‐meteoroid collisions and accelerated by the magnetized solar wind, twist the interplanetary magnetic field as they are accelerated. Depending on the cloud size, minutes to hours‐long perturbations are observed by interplanetary spacecraft and have been termed interplanetary field enhancements (IFEs). This dust‐solar wind interaction hypothesis is supported by reconstructed magnetic field geometry using IFEs detected nearly simultaneously by multiple spacecraft. On 16 January 2018, an IFE was observed by eight spacecraft in the solar wind and later by four spacecraft in the magnetosheath, after the IFE crossed the bow shock. The post‐shock magnetic field geometry differed from that in the solar wind in a way compatible with the dust cloud having interacted with the shocked solar wind. These observations of dust clouds surfing in the solar wind show us how submicron‐sized interplanetary dust is transported and ultimately expelled from the solar system.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.014
GPT teacher head0.266
Teacher spread0.253 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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