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Record W4393046774 · doi:10.1029/2023gl106788

Magnetic Field Signatures of Craters on Mars

2024· article· en· W4393046774 on OpenAlexafffund
Anna Mittelholz, Sarah Steele, Roger Fu, C. L. Johnson, R. J. Lillis, Gaia Stucky de Quay

Bibliographic record

VenueGeophysical Research Letters · 2024
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
KeywordsMars Exploration ProgramImpact craterGeologyGeophysicsAstrobiologyField (mathematics)Earth scienceSeismologyPhysics

Abstract

fetched live from OpenAlex

Abstract Craters on Mars are a window into Mars' past and the time they were emplaced. Because the crust is heated and shocked during impact, craters can demagnetize or magnetize the crust depending on the presence or absence of a dynamo field at the time of impact. This concept has been used to constrain dynamo timing. Here, we investigate magnetic anomalies associated with craters larger than 150 km. We find that most of those craters, independent of age, exhibit demagnetization signatures in the form of a central magnetic low. We demonstrate a statistically significant association between such signatures and craters, and hypothesize that the excavation of strongly magnetic crustal material may be an important contribution to the dominance of demagnetized craters. This finding implies that the simple presence or absence of crater demagnetization signatures is not a reliable indicator for the activity of the Martian dynamo during or after crater formation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.203
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.301
Teacher spread0.277 · 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.

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

Citations10
Published2024
Admission routes2
Has abstractyes

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