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Record W4389274624 · doi:10.1029/2023gl104790

Gravitational Constraints on the Earth's Inner Core Differential Rotation

2023· article· en· W4389274624 on OpenAlexafffund
Hugo Lecomte, S. Rosat, Mioara Mandéa, Mathieu Dumberry

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Alberta
FundersEuropean Research CouncilUniversité de StrasbourgCentre National d’Etudes SpatialesCentre National de la Recherche ScientifiqueCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsInner coreAmplitudePhysicsDifferential rotationEarth's rotationGravitationGeodesyRotation (mathematics)Gravitational fieldMantle (geology)GeologyGeophysicsGeometryClassical mechanicsOpticsAstrophysicsMathematics

Abstract

fetched live from OpenAlex

Abstract The differential axial rotation of the solid inner core (IC) is suggested by seismic observations and expected from core dynamics models. A rotation of the IC by an angle α takes its degree 2, order 2 topography (peak‐to‐peak amplitude δh ) out of its gravitational alignment with the mantle. This creates a gravity variation of degree 2, order 2 proportional to δh and to α . Here, we use gravity observations from Satellite Laser Ranging, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On to reconstruct the time‐variable S 2,2 Stokes coefficient. We show that for δh = 90 m, S 2,2 provides upper bounds on α of 0.09°, 0.3°, and 0.4° at periods of ∼4, ∼6, and ∼12 years, respectively. These are overestimates, as our reconstructed S 2,2 signal likely remains polluted by hydrology, although viscous relaxation of the IC can permit larger amplitudes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.008

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 head0.096
GPT teacher head0.311
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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations5
Published2023
Admission routes2
Has abstractyes

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