Resolving 70 Million Years of Earth's True Polar Wander and Precession: Paleomagnetic Validation of a Seismic Tomography–Based Mantle Convection Model
Bibliographic record
Abstract
Mantle convection alters Earth’s ellipsoidal shape and modifies its moment of inertia, leading to rotation-axis shifts known as true polar wander (TPW). By combining seismic tomography with the Back-and-Forth Nudging (BFN) method, we created a time-dependent convection model that reconstructs mantle density evolution and Earth’s moment of inertia over the last 70 million years. This modeling framework closely agrees with independent paleomagnetic data on Cenozoic changes in Earth’s rotation pole, notably reproducing the previously unexplained U-turn in TPW around 50 million years ago. Our results show that TPW can exceed five degrees, despite stabilizing factors such as high viscosity in the lower mantle and Earth’s remnant rotational bulge. Verification of predicted variations in Earth’s ellipsoidal figure, based on paleomagnetic constraints, provides a robust reference point for forecasting convection-induced dynamic flattening. Over the 70-million-year interval, we document changes in flattening that range from -0.2% to +0.1% during the Paleogene. Furthermore, our predictions of Paleogene axial precession frequency align with recent independent cyclostratigraphic analyses, offering strong evidence for the accuracy of our model and reinforcing the hypothesis of diminished luni-solar tidal dissipation during that period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".