Phase Transitions of Solid and Liquid Fe-Si Alloys with Applications to Planetary Core Composition and Dynamo Processes
Bibliographic record
Abstract
It is widely accepted that the convection of the liquid metallic outer core is the driver of the dynamo-produced magnetic field in terrestrial bodies, yet the core composition and the processes which occur within the core are difficult to study due to the extreme temperatures (T) and pressures (P). By examining the phase changes that occur with varying P, T, and composition (X), phase diagrams may be constructed for expected core mimetic compositions. The constructed phase diagrams of Fe-Si alloys along with known or modeled P,T conditions of solid/liquid phases within the cores of interest can then be used to determine likely compositions of these cores.Experiments were conducted in a 1000-ton cubic anvil press at P in the range 2-5 GPa and T into the liquid state. A central 5-hole BN cylinder held 5 different Fe-Si sample compositions simultaneously with a thermocouple located at the base of the BN cylinder, and was surrounded by a graphite furnace within a pyrophyllite cubic pressure cell. Fe-Si samples were prepared from pure Fe up to 33 wt% Si using mixtures of powders with known compositions. Following quenching of each experiment, the samples underwent electron microprobe analysis and along with textural analyses, these are used to map the T-X phase diagram at constant P. These phase diagrams will then be applied to the cores of small terrestrial bodies, such as the Moon, Mercury, and Vesta, to identify potential core compositions that are consistent with observational data and models that employ prescribed interior compositions and phases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".