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Record W2954838036 · doi:10.1029/2019ja026614

A Fast Fermi Acceleration at Mars Bow Shock

2019· article· en· W2954838036 on OpenAlexaff
K. Meziane, C. Mazelle, D. L. Mitchell, A. M. Hamza, E. Penou, B. M. Jakosky

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of New Brunswick
FundersCentre National d’Etudes Spatiales
KeywordsPhysicsBow shock (aerodynamics)Solar windShock (circulatory)ElectronBow waveFermi accelerationMartianMars Exploration ProgramComputational physicsMagnetopauseGeophysicsShock wavePlasmaParticle accelerationAstronomyMechanics

Abstract

fetched live from OpenAlex

Abstract We report, for the first time, strong evidences that a fast Fermi mechanism is taking place at the Mars bow shock. The MAVEN spacecraft observations from the Solar Wind Electron Analyzer instrument show electron flux spikes with energies up to ∼1.5 keV. These spikes are associated with sunward propagating electrons and appear when the interplanetary field line threading the spacecraft is connected near the Martian bow shock tangency point. The observed loss cone distribution is a salient feature of these backstreaming electrons as the phase space density peaks on a ring centered along the magnetic field direction. Moreover, the data show no evidence of any effect due to a hypothetical cross‐shock electric potential on the observed angular distributions. Although similar distributions are seen at the terrestrial bow shock, the quantitative analysis of the measurements strongly indicates that the electrons are produced at the shock foot and escape upstream before exploring the entire shock structure.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.330
Teacher spread0.281 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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