Electrical resistivity of the Fe–Si–S ternary system: implications for timing of thermal convection shutdown in the lunar core
Bibliographic record
Abstract
The composition of the lunar core has been suggested to be Fe-rich with varying amounts of lighter elements, such as Si and S. Presence of Si and S affects electrical and thermal transport properties and thus influences core thermal processes and evolution. Paleomagnetic observations constrain a high intensity magnetic field that ceases shortly after formation of the moon (~ 3.5-4.2 Ga year ago), and thermal convection in the core may contribute to generation of this field. In this study, the electrical resistivity of Fe-14 wt% Si-3 wt% S was measured in both solid and molten states at pressures up to 5 GPa and thermal conductivity was calculated via the Wiedemann-Franz Law from the electrical measurements. The results were used to estimate the adiabatic conductive heat flux of a molten Fe-14 wt% Si-3 wt% S lunar core and compared to a Fe-2-17 wt% Si lunar core, which showed that thermal convection of either core composition shuts down within the duration of the high intensity magnetic field: (1) 3.17-3.72 Ga year ago for a Fe-14 wt% Si-3 wt% S core; and (ii) 3.38-3.86 Ga years ago for a Fe-2-17 wt% Si core. Results favouring compatibility of these core compositions with paleomagnetic observations are strongly dependent on the temperature of the core-mantle boundary and time-dependent mantle-side heat flux.
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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".