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Record W2022546013 · doi:10.1029/2009je003528

Iron snow zones as a mechanism for generating Mercury's weak observed magnetic field

2010· article· en· W2022546013 on OpenAlexafffund
R. Vilim, S. Stanley, S. A. Hauck

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDynamoSnowGeophysicsField strengthMagnetic fieldDynamo theoryMercury (programming language)GeologyMercury's magnetic fieldSolar dynamoOuter coreConvectionMagnetismMagnetohydrodynamicsEarth's magnetic fieldInner coreAtmospheric sciencesPhysicsCondensed matter physicsMechanicsL-shellGeomorphology

Abstract

fetched live from OpenAlex

The anomalously weak observed magnetic field of Mercury is difficult to explain by appealing to crustal remanent magnetism or Earth‐like dynamo mechanisms. Although the field is likely caused by a hydromagnetic dynamo, the field strength is far weaker than the characteristic strength expected from an active, strong field dynamo. Recent experimental work has shown that sources of compositional convection exist in mixtures of sulfur and iron at temperatures and pressures relevant to Mercury's core. The number and location of these iron “snow” zones is dependent on the sulfur content of the liquid portion of the core. We use a numerical dynamo model to show that the core states which include a snow zone midway through the core produce the observed field strength and expected field partitioning of the Mercurian magnetic field.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.308
Teacher spread0.281 · 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 designBench or experimental
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

Citations78
Published2010
Admission routes2
Has abstractyes

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