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Record W4321493308 · doi:10.5194/egusphere-egu23-479

Thermal Convection in Vesta’s Core from Experimentally-Based Conductive Heat Flow Estimates

2023· preprint· en· W4321493308 on OpenAlexaff
Oluwasanmi A. Orole, Wenjun Yong, Richard A. Secco

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrical resistivity and conductivityHeat fluxThermal conductivityFlux (metallurgy)Inner coreMaterials scienceConvectionDynamoOuter coreThermalCore (optical fiber)Magnetic fieldDynamo theoryAdiabatic processThermodynamicsCondensed matter physicsHeat transferPhysicsMetallurgyComposite material

Abstract

fetched live from OpenAlex

Electrical resistivity measurements of Fe-5wt%Ni were made in-situ under pressures of 2-5 GPa and temperatures up to 2000 K in a cubic-anvil press. The thermal conductivity was calculated from the measured electrical resistivity data using the Wiedemann–Franz law. Comparison of these data with previous studies on pure Fe and Fe-10wt%Ni shows that a change in the Ni content within the range 0-10wt% Ni has no significant effect on electrical resistivity of Fe alloys.The thermal conductivity values of Fe-5wt%Ni from this study, was used to calculate the adiabatic heat flux in Vesta’s core. Vesta is of interest because the remnant magnetism in eucrites dated at 3.69Ga, reveals it possessed an internally generated dynamo (Fu et al., 2012). Comparing the estimated adiabatic core heat flux of ~331 MW at the top of Vesta’s core to the range of estimated heat flux through the CMB of 1.5–78 GW, we infer that the mechanism stirring Vesta’s liquid outer core to generate its surface magnetic field tens of millions of years in its early history was thermal convection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.282
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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