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Record W1976972218 · doi:10.1029/2004jb003495

A Green's function for the excitation of torsional oscillations in the Earth's core

2005· article· en· W1976972218 on OpenAlexaff
B. A. Buffett, J. E. Mound

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsOuter coreMantle (geology)GeophysicsInner coreNormal modeConvectionMagnetic fieldImpulse (physics)ExcitationMechanicsClassical mechanicsComputational physicsAcousticsQuantum mechanics

Abstract

fetched live from OpenAlex

Convection in the Earth's outer core excites torsional oscillations in the fluid, which couple to rigid‐body motion of the inner core and mantle. The torsional oscillations are detected as time variations of the magnetic field, whereas the motion of the mantle is observed as changes in the length of day. We develop a model for the motion of the core‐mantle system using a Green's function for the impulse response to a localized source of excitation. The response to a source which is distributed in both space and time is obtained by convolving the Green's function with the appropriate source function. The Green's function is constructed by summing the normal modes of the system. We derive an orthogonality condition for the normal modes and use it to determine the coefficients of the normal mode expansion. Examples of the Green's function are presented for a variety of source locations. The predictions may be compared with observations to provide insights into the convective processes that excite the oscillations. It may also be possible to recover the physical properties that determine the period of the normal modes, including the structure of the magnetic field inside the outer core and the nature of coupling at the fluid boundaries. We outline a strategy for inverting the observations and identify potential difficulties due to the nonuniqueness of the inverse problem.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.329
Teacher spread0.289 · 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 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

Citations28
Published2005
Admission routes1
Has abstractyes

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