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Record W2752459840 · doi:10.1029/2017jb015394

Direct Observation of Faulting by Means of Rotary Shear Tests Under X‐Ray Micro‐Computed Tomography

2018· article· en· W2752459840 on OpenAlexafffund

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCanada Foundation for InnovationCarbon Management CanadaCMG Reservoir Simulation Foundation
KeywordsSlip (aerodynamics)Shear (geology)Surface finishSurface roughnessInterlockingTomographyContact areaPower law

Abstract

fetched live from OpenAlex

Abstract Friction and fault evolution are critical aspects in earthquake studies as they directly influence the nucleation, propagation, and arrest of earthquake ruptures. We present the results of a recently developed experimental approach that investigates these important aspects using a combination of rotary shear testing and X‐ray micro‐computed tomography technology. Two sets of experiments at normal stresses ( σ n ) of 2.5 and 1.8 MPa were conducted on synthetic laboratory faults. We identified real contact areas ( A c ) on the fault surfaces and estimated sizes of contact patches by means of micro‐computed tomography image analysis. The number of contact patches and their sizes showed positive correlations with σ n , and contact patch size distributions followed power law relations. The total number of contact patches decreased with increasing slip distance, and large contact patches endured longer slip distance than small ones. Secondary off‐fault fractures created by interlocking and breakdown of large contact patches were closely related to the sudden drops of frictional resistance, suggesting the dominant role of surface roughness on shear behavior especially at low stress.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.317
Teacher spread0.267 · 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

Citations38
Published2018
Admission routes2
Has abstractyes

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