Bibliographic record
Abstract
Abstract The tilt angle of the 18.6 year precession of the Moon's solid inner core is unknown, but it is set by a balance between gravitational and pressure torques acting on its elliptical figure. We show here that to first order, the angle of precession of the inner core of a planetary body is determined by the frequency of the free inner core nutation, ω ficn , relative to the precession frequency, Ω p . If | ω ficn |≪|Ω p |, the inner core is blind to the gravitational influence of the mantle. If | ω ficn |≫|Ω p |, the inner core is gravitationally locked to the mantle and is nearly aligned with it. If ω ficn ≈Ω p , large inner core tilt angles can result from resonant excitation. Viscous inner core relaxation and electromagnetic coupling can attenuate large tilt angles. For the specific case of the Moon, we show that ω ficn is to within a factor of 2 of Ω p = 2 π /18.6 yr −1 . For a rigid inner core, this implies a tilt of 2 to 5° with respect to the mantle, and larger if ω ficn is very close to Ω p . More modest tilt angles between 0 and 0.5° result if viscous relaxation within the inner core occurs on a timescale of one lunar day. Predictions from our model may be used in an attempt to detect the gravity signal resulting from a tilted inner core, to determine the past history of the inner core tilt angle, and to assess models of dynamo generation powered by differential rotation at the core‐mantle and inner core boundaries.
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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 | 0.000 |
| 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".