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Record W3088608772 · doi:10.1029/2020jb021040

A Multiscale Numerical Modeling Investigation on the Significance of Flow Partitioning for the Development of Quartz c‐Axis Fabrics

2021· article· en· W3088608772 on OpenAlexaff
Ankit Bhandari, Dazhi Jiang

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2021
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsMicroscale chemistryVorticityQuartzIsotropyRheologyAnisotropyMechanicsFlow (mathematics)Materials scienceGeologyGeometryVortexPhysicsComposite materialOpticsMathematics

Abstract

fetched live from OpenAlex

Abstract Quartz c‐axis fabrics in natural mylonites can vary to such an extent that they apparently give opposite senses of shear in a single thin section. Many hypotheses have been invoked to explain this. Here, we couple our self‐consistent multiscale approach for flow partitioning with the viscoplastic self‐consistent model for c‐axis fabric simulation to investigate quartz c‐axis fabric development. Quartz aggregates are regarded as microscale Eshelby inhomogeneities embedded in a macroscale medium whose effective rheology is represented by a hypothetical homogeneous equivalent medium which is assumed to be rheologically isotropic or has a planar anisotropy. We reproduced the observed quartz c‐axis fabrics. We found that although the microscale flow fields are distinct from one another and from the macroscale flow, the microscale vorticity in every inhomogeneity has the same sense as the macroscale vorticity. This implies that one can use the average of the microscale vorticity axes determined through the crystallographic vorticity axis analysis to obtain the macroscale vorticity axis. However, quartz c‐axis fabrics cannot be used to determine the vorticity number where flow partitioning is significant.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.191
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.091
GPT teacher head0.318
Teacher spread0.227 · 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 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

Citations9
Published2021
Admission routes1
Has abstractyes

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