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Record W2006386293 · doi:10.1029/2005je002424

Viscous and impact demagnetization of Martian crust

2007· article· en· W2006386293 on OpenAlexafffund
H. Shahnas, Jafar Arkani‐Hamed

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMcGill UniversityUniversity of Toronto
FundersGoddard Space Flight CenterNatural Sciences and Engineering Research Council of CanadaInstitut de Physique du Globe de ParisUniversity of Toronto
KeywordsImpact craterMartianGeologyDemagnetizing fieldCrustGeophysicsMars Exploration ProgramDynamoMagnetizationAstrobiologyPhysicsMagnetic field

Abstract

fetched live from OpenAlex

Magnetization of Martian crust has been modified by impact‐induced shock waves and viscous decay since the cessation of the core dynamo of Mars at around 4 Gyr ago. Thermal evolution models of Mars suggest that the potentially magnetic layer was about 85 km thick during the active period of the core dynamo, assuming magnetite as the major magnetic carrier. The lower boundary of the magnetic layer has gradually decreased, by a total of about 30 km, through viscous decay of magnetization. The large impacts that created the giant basins Hellas, Argyre, and Isidis have almost completely demagnetized the crust beneath the basins. The shock wave pressure produced by impacts that created craters of diameters 300–1000 km is expected to significantly demagnetize the crust beneath the craters. However, except for a few craters, there is no signature of appreciable demagnetization. This implies that either the magnetic carriers have high coercivity and have resisted demagnetization, or magnetic source bodies are deep seated, or they have acquired magnetization after the intensive impact cratering period. An alternate possibility is that the scaling laws proposed for small craters do not apply to the large craters considered in this paper.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.335
Teacher spread0.310 · 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 designObservational
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

Citations37
Published2007
Admission routes2
Has abstractyes

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