Modeling of nutation and precession: Effects of electromagnetic coupling
Bibliographic record
Abstract
The presence of an internal magnetic field influences of the Earth's nutation through the effects of electromagnetic torques at the boundaries of the fluid core. We calculate the effect of electromagnetic torques on nutation by combining a solution for the full hydromagnetic response of the fluid core with the nutation theory of Mathews et al. [2002] . The coupling of the fluid outer core to the mantle and solid inner core is described by two complex constants, KCMB and KICB, that characterize the electromagnetic torques at the core‐mantle boundary (CMB) and the inner core boundary (ICB). Predictions for KCMB and KICB are compared with estimates inferred from observations of the Earth's nutation. The estimate of KCMB can be explained by the presence of a thin conducting layer at the base of the mantle with a total conductance of 108 S. The overall root‐mean‐square (RMS) radial field at the CMB is 0.69 mT, which is partitioned into a dipole component (0.264 mT) and a nondipole component (0.64 mT). (The latter is represented using a uniform radial field.) The estimate of KICB can be explained with a mixture of dipole and nondipole components. The overall RMS field at the ICB is 7.17 mT, though smaller values are inferred when small adjustments are made to the dynamic ellipticity of the inner core and/or the fluid density at the boundary. The minimum RMS radial field required to explain the nutation observations is 4.6 mT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".