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Record W4400290480 · doi:10.5194/epsc2024-664

A Prediction of the Inter-Annual Variability of Sulfur in Mercury’s Exosphere and Subsurface

2024· preprint· en· W4400290480 on OpenAlexaff
Sebastien Verkercke, Jean-Yves Chaufray, François Leblanc, Liam S. Morrissey, Michael Phillips, Giovanni Munaretto

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMercury (programming language)ExosphereSulfurChemistryEnvironmental chemistryEnvironmental scienceComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Since the discovery of atoms ejected from Mercury’s surface and forming a tenuous atmosphere around the planet, Mercury’s surface-exosphere interface has been extensively observed by both on-ground and space-borne instruments. Between 2007 and 2011, the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft performed three fly-bys of Mercury followed by four years in orbit around it. This mission derived the surface composition of Mercury which notably includes some moderately volatile species such as sodium, potassium or sulfur. While the two former were clearly identified in Mercury’s exosphere, the latter has never been observed as released from the surface in its neutral form. Moreover, recent studies suggest that hollows, i.e. bright, shallow flat-floored depressions, could potentially be formed by local sulfur accumulation This suggests that sulfur should be present in both the surface and the exosphere of Mercury, with associated migration and/or diffusion processes that could sustain such geological features. Using a 3-D Exospheric Global Model with a Monte-Carlo test-particles approach and accounting for species diffusion in the first meter of Mercury’s regolith, this study aims for the first global prediction of the inter-annual variability of neutral sulfur density in both Mercury’s exosphere and subsurface. This work is particularly relevant for the preparation of ESA/BepiColombo mission which will start its scientific mission in December 2025.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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