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Record W6912159131 · doi:10.5281/zenodo.14740493

Resolving 70 Million Years of Earth's True Polar Wander and Precession: Paleomagnetic Validation of a Seismic Tomography–Based Mantle Convection Model

2025· article· en· W6912159131 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsPaleomagnetismMantle (geology)Mantle convectionFlatteningConvectionPolar wanderGeodynamicsPolarMoment of inertia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicHistory of Computing TechnologiesFrench-language works237,207