A Past Lunar Dynamo Thermally Driven by the Precession of Its Inner Core
Bibliographic record
Abstract
Abstract The Cassini state equilibrium associated with the precession of the Moon predicts that the mantle, fluid core, and solid inner core precess at different angles. We present estimates of the dissipation from viscous friction associated with the differential precession at the core‐mantle boundary (CMB), Qcmb, and at the inner core boundary (ICB), Qicb, as a function of the evolving lunar orbit. We focus on the latter and show that, provided the inner core was larger than 100 km, Qicb may have been as high as 1010–1011 W for most of the lunar history for a broad range of core density models. This is larger than the power required to maintain the fluid core in an adiabatic state; therefore, the heat released by the differential precession at the ICB can drive a past lunar dynamo by thermal convection. This dynamo can outlive the dynamo from precession at the CMB and may have shut off only relatively recently. Estimates of the magnetic field strength at the lunar surface are of the order of a few μT, compatible with the lunar paleomagnetic intensities recorded after 3 Ga. We further show that it is possible that a transition of the Cassini state associated with the inner core may have occurred as a result of the evolution of the lunar orbit. The heat flux associated with Qicb can be of the order of a few mW m−2, which should slow down inner core growth and be included in thermal evolution models of the lunar core.
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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".