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Record W3137361415 · doi:10.1029/2020gc009615

Formation of Continental Microplates Through Rift Linkage: Numerical Modeling and Its Application to the Flemish Cap and Sao Paulo Plateau

2021· article· en· W3137361415 on OpenAlexaff
Derek Neuharth, Sascha Brune, Anne Glerum, Christian Heine, J. Kim Welford

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMemorial University of Newfoundland
FundersShell Global Solutions InternationalShellNational Science Foundation
KeywordsRiftGeologyContinental marginBreakupLithosphereSeismologyContinental crustPaleontologyPlate tectonicsExtensional definitionGeophysicsTectonicsMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract Continental microplates are enigmatic plate boundary features, which can occur in extensional and compressional regimes. Here we focus on microplate formation and their temporal evolution in continental rift settings. To this aim, we employ the geodynamic finite element software ASPECT to conduct 3D lithospheric‐scale numerical models from rift inception to continental breakup. We find that depending on the strike‐perpendicular offset and crustal strength, rift segments connect or interact through one of four regimes: (1) an oblique rift, (2) a transform fault, (3) a rotating continental microplate or (4) a rift jump. We highlight that rotating microplates form at offsets >200 km in weak to moderately strong crustal setups. We describe the dynamics of microplate evolution from initial rift propagation, to segment overlap, vertical‐axis rotation, and eventually continental breakup. These models may explain microplate size and kinematics of the Flemish Cap, the Sao Paulo Plateau, and other continental microplates that formed during continental rifting worldwide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.195
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations85
Published2021
Admission routes1
Has abstractyes

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