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Record W3034748690 · doi:10.1029/2019je006183

Fault Structure and Origin of Compressional Tectonic Features Within the Smooth Plains on Mercury

2020· article· en· W3034748690 on OpenAlexafffund
G. A. Peterson, C. L. Johnson, P. K. Byrne, R. J. Phillips

Bibliographic record

VenueJournal of Geophysical Research Planets · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
FundersMcDonnell Center for the Space Sciences, Washington University in St. LouisNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaNational Aeronautics and Space Administration
KeywordsGeologyLithosphereTectonicsLayeringVolcanoSeismologyVolcanismThrust faultGeometryPetrology

Abstract

fetched live from OpenAlex

Abstract The major physiographic “smooth plains” units on Mercury are dominantly composed of volcanic deposits that have been deformed by horizontal compressive stresses. An open issue is whether these features formed by stresses induced by global contraction, bending stresses due to volcanic loading, or some combination of both. In this study, we model the surface expression of 12 shortening structures within several smooth plains units across Mercury to determine the geometries of the underlying faults. We implement an elastic dislocation model, using both listric and planar fault geometries, to place estimates on the depth of faulting for each feature. We show that a majority of smooth plains shortening structures penetrate the lithosphere to depths greater than 15 km. Thrust faults of this scale have not previously been recognized within the planet's smooth plains units and require a large horizontal stresses to form, which is best explained if this stress arises from global contraction. Further, our results suggest that the observed relief and length contrast between features in the smooth plains units and older intercrater plains units can be explained by interior layering of, and/or a shallower brittle‐ductile transition underlying, the smooth plains units.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.390

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 integrity0.0000.001
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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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