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Record W4411109258 · doi:10.1029/2025gc012194

Thickening Cratonic Lithosphere by Horizontal Compression in the Presence of Surface Erosion and Sedimentation

2025· article· en· W4411109258 on OpenAlexafffund
Kristina Kublik, Claire A. Currie, D. Graham Pearson

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaMaterials and Energy Research CenterCanada First Research Excellence FundUniversity of Alberta
KeywordsGeologyThickeningLithosphereSedimentationErosionCompression (physics)GeochemistryGeomorphologyEarth scienceTectonicsSeismologySediment

Abstract

fetched live from OpenAlex

Abstract Here we examine how lithospheric thickening is affected by active surface sedimentation and erosion in geodynamic models of craton formation—an aspect that has been neglected in previous models even though cratons may be the first landmasses to emerge above sea level. In our two‐dimensional numerical models, inward horizontal velocities are imposed at the side boundaries of the model domain to induce thickening of the cratonic lithosphere by horizontal compression. Various rates of sedimentation and erosion are applied at the surface and the thickness of the lithosphere is monitored during the 50 Myr compression phase, and for 2 billion years after the imposed compression phase. In our models, surface processes act on the high‐relief surface topography of the mobile belts adjacent to the cratonic nucleus. Erosion in the mobile belts during the compression alters lithosphere geodynamics, increasing the thickness of the mobile belt lithosphere to depths capable of supporting diamond growth. This enhanced thickening in the mobile belt regions limits shortening and thickening of the cratonic nucleus and the lithospheric thickness can vary by up to 15 km between models with different surface process rates.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.998

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)0.0000.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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