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Record W3102840025 · doi:10.5194/epsc2020-421

Upstream Ultra-Low Frequency Waves Observed by MESSENGER's Magnetometer: Implications for Particle Acceleration at Mercury's Bow Shock

2020· article· en· W3102840025 on OpenAlexaff
Norberto Romanelli, G. A. DiBraccio, D. J. Gershman, G. Le, C. Mazelle, K. Meziane, J. A. Slavin, J. M. Raines, Austin N. Glass, J. R. Espley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsForeshockPhysicsSolar windUltra low frequencyAmplitudePopulationMagnetometerPolarization (electrochemistry)GeophysicsComputational physicsAstrophysicsMagnetic fieldAstronomySeismologyOpticsGeologyChemistry

Abstract

fetched live from OpenAlex

In this work we perform the first statistical analysis of the main properties of waves observed in the 0.05–0.41 Hz frequency range in the Hermean foreshock by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Magnetometer. Although we find similar polarization properties to the '30 s' waves observed at the Earth's foreshock, the normalized wave amplitude (∼0.2) and occurrence rate (∼0.5%) are much smaller. This suggests significant lower backstreaming proton fluxes, due to the relatively low solar wind Alfvenic Mach number around Mercury. These differences could also be related to the relatively smaller foreshock size and/or more variable solar wind conditions. Furthermore, we estimate that the speed of resonant backstreaming protons in the solar wind reference frame (likely source for these waves) ranges between 0.95 and 2.6 times the solar wind speed. The closeness between this range and what is observed at other planetary foreshocks suggests that similar acceleration processes are responsible for this energetic population and might be present in the shocks of exoplanets.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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