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Record W1598253851 · doi:10.1002/2013je004603

Impact heating and coupled core cooling and mantle dynamics on Mars

2014· article· en· W1598253851 on OpenAlexafffund
J. H. Roberts, Jafar Arkani‐Hamed

Bibliographic record

VenueJournal of Geophysical Research Planets · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsDynamoMantle (geology)Mars Exploration ProgramNoachianGeophysicsGeologyConvectionMantle convectionInner coreStratification (seeds)Dynamo theoryThermalAstrobiologyLithosphereMartianMechanicsMagnetic fieldPhysicsMeteorologyPaleontology

Abstract

fetched live from OpenAlex

Abstract Several giant impact basins of mid‐Noachian age have been identified on Mars, and the global magnetic field appears to have vanished at about the same time. The impacts that formed these basins delivered a large amount of heat to the planetary interior, modified the pattern of mantle convection, and suppressed core cooling, potentially contributing to the cessation of dynamo activity. Here we investigate the thermal evolution of Mars in response to the largest basin‐forming impacts, using a new method of coupling models of mantle convection with parameterized core cooling. We find that heating by a large impact generates a strong hemispheric upwelling in the mantle, which quickly spreads into a warm layer beneath the stagnant lid. The impact heating of the core leads to spherically symmetric stratification of the core; the outermost layers are strongly heated. This acts as a thermal “blanket” that prevents cooling of the interior and shuts down core convection. While the hottest part of the thermal blanket in the outermost core disappears relatively quickly, dynamo activity does not restart for ~100 Myr, and the core does not return to a fully convective state for ~1 Gyr following the impact.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.237

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 head0.042
GPT teacher head0.347
Teacher spread0.305 · 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

Citations34
Published2014
Admission routes2
Has abstractyes

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