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Record W4280588564 · doi:10.3389/fspas.2022.895362

The Martian Crustal Magnetic Field

2022· article· en· W4280588564 on OpenAlexafffund
Anna Mittelholz, C. L. Johnson

Bibliographic record

VenueFrontiers in Astronomy and Space Sciences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule ZürichHarvard University
KeywordsDynamoMartianGeophysicsMagnetizationMars Exploration ProgramAstrobiologyCrustPlanetMartian surfacePhysicsMagnetismField (mathematics)Earth's magnetic fieldGeologyMagnetic fieldAstrophysicsCondensed matter physics

Abstract

fetched live from OpenAlex

Mars’ crustal magnetic field holds information on the planet’s interior evolution and exterior processes that have modified the crust. Crustal magnetization records an ancient dynamo field that indicates very different interior conditions in the past, possibly linked to the presence of a thicker early atmosphere. Current data sets have provided a wealth of information on the ancient magnetic field, and on the acquisition and modification of magnetization in the crust. However, many puzzles remain regarding the nature and origin of crustal magnetization, and the timing and characteristics of the past dynamo. Here we use recent advances in understanding martian magnetism to highlight open questions, and ways in which they can be addressed through laboratory analysis, modeling and new data sets. Many of the outstanding key issues require data sets that close the gap in spatial resolution between available global satellite and local surface magnetic field measurements. Future missions such as a helicopter, balloon or airplane can provide areal high resolution coverage of the magnetic field, vital to major advances in understanding planetary crustal magnetic fields.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 designNot applicable
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

Citations25
Published2022
Admission routes2
Has abstractyes

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