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Record W4407175013 · doi:10.1029/2024gl113926

Mars' Hemispheric Magnetic Field From a Full‐Sphere Dynamo

2025· article· en· W4407175013 on OpenAlexafffund
Chi Yan, Ankit Barik, S. Stanley, Anna Mittelholz, Ana‐Catalina Plesa, C. L. Johnson

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Aeronautics and Space Administration
KeywordsDynamoMars Exploration ProgramDynamo theoryGeophysicsMercury's magnetic fieldPhysicsMagnetic fieldField (mathematics)GeologyAstrobiologyGeodesyAstronomyEarth's magnetic fieldL-shellMathematics

Abstract

fetched live from OpenAlex

Abstract Seismic measurements from the NASA Mars InSight mission revealed that Mars' core has a relatively low density, implying a larger fraction of lighter elements than previously thought, which further leads to a low melting temperature. Thus, Mars probably never developed a solid inner core during its early history when the dynamo was active. We perform full‐sphere dynamo simulations to eliminate the influence of an inner core on dynamo behaviors and investigate how various magnitudes of heat flux perturbations at the core‐mantle boundary affect the field morphology, comparing results to those from models with small inner cores. We find that a hemispheric magnetic field can result when the heat flux is concentrated in one hemisphere. Moreover, a dynamo model without the presence of an inner core can better explain Mars' crustal magnetic field dichotomy than that in a spherical shell surrounding a solid inner core.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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