Carbon storage in Earth's deep interior implied by carbonate-silicate-iron melt miscibility
Bibliographic record
Abstract
Carbonate melts have been proposed to exist in the lower mantle, but their interaction with other lower mantle melt compositions is poorly understood. To understand miscibility in the carbonate-silicate-metal melt system, we simulate endmember, binary, and ternary melt mixtures and study how their Gibbs free energies of mixing evolve with pressure. We find that carbonate-metal and carbonate-silicate melts have miscibility gaps that close with increasing pressure, while silicate-metal melts are immiscible at all lower-mantle pressures. Extending this analysis to the core-mantle boundary, we suggest three miscible melt fields near the endmember carbonate, silicate, and iron melt compositions. Analysis of the densities of these miscible melt compositions indicates that some carbonate-rich and some silicate-rich melt compositions are gravitationally stable at the core-mantle boundary and could be candidate compositions to explain ultra-low velocity zones. Additionally, we evaluate the speciation of an example immiscible melt composition at various pressures throughout the mantle and identify reduced carbon species that would be expected to form in the melt. Our analysis reveals that a majority of Earth's carbon could have been transported to the core during core-mantle differentiation and that much of Earth's carbon may be stored in the deep interior today.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".