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Record W2972436466 · doi:10.1029/2019gl084485

The Iron Invariance: Implications for Thermal Convection in Earth's Core

2019· article· en· W2972436466 on OpenAlexafffund
Wenjun Yong, Richard A. Secco, Joshua A. H. Littleton, Reynold E. Silber

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Stroke FoundationCanada Foundation for Innovation
KeywordsDynamo theoryInner coreGeophysicsOuter coreConvectionThermal conductivityEarth's magnetic fieldThermalCore–mantle boundaryNatural convectionHeat fluxElectrical resistivity and conductivityGeologyThermodynamicsMechanicsHeat transferMantle (geology)DynamoPhysicsMagnetic field

Abstract

fetched live from OpenAlex

Abstract Convection of the liquid iron (Fe) outer core and electrical properties of Fe are responsible for the geodynamo that generates the geomagnetic field. Recent results showed the thermal conductivity of the core and related conductive heat flux may be much larger than previously accepted, suggesting that thermal convection would not be an energy source to power the geodynamo. Here we report experimental measurements of the electrical resistivity of solid and liquid Fe which show invariant values along the melting boundary at pressures up to 24 GPa. The observed resistivity invariance was extrapolated to Earth's predominantly Fe solid inner core and liquid outer core conditions and, using the Wiedemann‐Franz law, the thermal conductivity was calculated. We calculate a conductive core heat flow of 8–9 TW at the core‐mantle boundary. These results provide strong support for thermal convection as a geodynamo energy source.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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