Self-consistent formation and thermochemical evolution of Mars’ basal mantle layer
Bibliographic record
Abstract
Seismic data recorded during the InSight mission (Banerdt et al., 2020) have shown that the Martian mantle is distinctly layered. At the core-mantle boundary, a molten silicate layer sits on top of the core that is believed to be enriched in iron and heat-producing elements (HPE), followed by a mushy layer above where melt fractions can be as high as ~60%, which then transitions to the solid mantle (Samuel et al., 2023). Previous numerical studies on the thermochemical evolution of planetary mantles after magma ocean crystallization have shown that stable stratification can occur in the lowermost mantle under certain conditions (e.g. Tosi et al., 2013; Plesa et al., 2014; Ballmer et al., 2017; Samuel et al., 2021), with the most important factor being that density contrast from chemical enrichment in the basal layer is much larger than those from thermal effects. However, most of these studies only perform numerical simulations of the solid mantle right after the magma ocean has crystallized (or during crystallization from a predetermined depth as in Ballmer et al. (2017)), and assume an initial structure that is unstable corresponding to a bottom-up fractional crystallization scenario. In this study, we begin from a compositionally homogeneous mushy Martian mantle and simulate its thermochemical evolution while considering the dynamics of melt percolation, compaction, freezing/melting, and chemical/HPE partitioning similar to Boukaré et al. (2025). Our preliminary results show that (1) it is possible to produce an enriched melt layer at the base of the mantle using a more self-consistent approach with initial conditions prior to full crystallization (Fig. 1), and (2) the degree of stable stratification in the basal melt layer (BML) depends significantly on the amount and timing of iron delivered to the CMB. Furthermore, the survival of the BML depends on how much it can resist erosion from crystallization and iron enrichment at the top of the layer (e.g. Laneuville et al., 2018). Overall, the presence and behavior of the BML play an important role in dictating the heat flux between the core and mantle, providing clues on the thermal and magnetic history of the red planet.Figure 1: Snapshots of different fields during the solidification of the Martian mantle from an initial mushy state at ~2350 Myr. a. Temperature, b. Melt fraction, c. HPE amount in ppb, and d. FeO concentration. A melt layer enriched in HPEs and iron is found at the bottom.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".