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Record W2124325663 · doi:10.1002/2014ja020907

Properties of the lunar wake predicted by analytic models and hybrid‐kinetic simulations

2015· article· en· W2124325663 on OpenAlexafffund
Hossna Gharaee, R. Rankin, R. Marchand, J. Paral

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyWestern Canada Research GridCompute CanadaNational Aeronautics and Space AdministrationNational Center for Atmospheric ResearchNational Science Foundation
KeywordsWakeSolar windKinetic energyPhysicsSpacecraftPlasmaTangentMechanicsTransverse planeClassical mechanicsComputational physicsGeometryMathematicsAstronomy

Abstract

fetched live from OpenAlex

Abstract An approximate model derived by Hutchinson (2008a), describing the interaction between the solar wind and the Moon, is applied to describe plasma in the lunar wake. The model accounts for plasma entering the wake region from two tangent points around the Moon. Cylindrical geometry is assumed, along with a strong constant magnetic field, and fixed transverse velocity and temperature. Under these approximations two angle‐dependent equations for fluid flow are obtained, which can be solved using the method of characteristics to provide the density inside the wake region. It is demonstrated that the model valid under these assumptions provides excellent agreement with observations from the ARTEMIS mission and with large‐scale hybrid‐kinetic plasma simulations. The model provides a practical alternative to kinetic simulations and is generally useful for determining properties of the lunar wake under different solar wind conditions. It will be useful as well for predicting properties of the plasma environment around unmagnetized bodies that have not yet been visited by spacecraft.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.079
GPT teacher head0.307
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2015
Admission routes2
Has abstractyes

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