Electrical Resistivity Measurements of Fe‐Si With Implications for the Early Lunar Dynamo
Bibliographic record
Abstract
Abstract The effect of impurities on the electrical resistivity of Fe at core conditions is controversial due to the challenges of measuring transport properties of Fe alloys under high‐pressure and high‐temperature conditions. In this study we describe an innovative technique to make wires of Fe‐Si samples initially in powder form for measuring electrical resistivity. The electrical resistivity of Fe‐2, Fe‐8.5, and Fe‐17 wt%Si was measured at 3, 4, and 5 GPa at temperatures into the liquid state and compared to results of Fe‐4.5 wt%Si and Fe from prior studies. Isothermal electrical resistivity increases linearly with increasing Si content. Yet the effect of Si content on the magnitude of the electrical resistivity compared to that of Fe diminished as temperature increased at all experimental pressures. This implies the contribution to the electrical resistivity due to Si impurities is dependent on temperature, in disagreement with Matthiessen's rule. Thermal conductivity of Fe‐Si alloys calculated using the Wiedemann‐Franz law indicates a nonnegligible influence of the Si content on the thermal conductive properties of Fe‐Si alloys. The results are used to calculate the adiabatic heat flux of an Fe‐Si lunar core and date the end of the high magnetic field intensity era of the lunar dynamo to be in the range 3.32–3.80 Ga.
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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) | 0.000 | 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".