Geochemical Advances in Mercury Science Facilitated by a Landed Mission
Bibliographic record
Abstract
The data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft have revealed several surprising characteristics about the surface of Mercury, leading to its classification as a geochemical endmember among the terrestrial planets. Some of these features include elevated abundances of up to 3 wt% S, C enrichment as high as 4 wt% over the local mean in low reflectance materials (LRM), Na up to 5 wt% at high northern latitudes, and Fe abundances typically lower than 2 wt% [e.g., 1–4]. The S and Fe concentrations have been used to infer that Mercury’s igneous history evolved under highly reduced oxygen fugacity conditions between 2.6 and 7.3 log10 units below the iron-wustite buffer [e.g., 5], which is more reducing than any other terrestrial planet in the solar system [e.g., 6]. This highly reduced nature has important consequences for the differentiation and thermal/magmatic evolution of Mercury. While the immense amount of data collected by MESSENGER revealed Mercury as a geochemical endmember, this new knowledge gained raised additional questions that necessitate continued exploration of the planet. Fortunately, BepiColombo launched in October of 2018, and this joint ESA/JAXA dual-orbiter spacecraft is the most ambitious effort yet attempted to explore Mercury [e.g., 7]. Looking beyond BepiColombo, there are major aspects of Mercury’s geochemical character and evolution for which significant knowledge gaps can be dramatically improved with data acquired from the planet’s surface via in situ landed science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".