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Record W2606653198 · doi:10.1139/cgj-2016-0213

Capillary forces between equally sized moving glass beads: an experimental study

2017· article· en· W2606653198 on OpenAlexvenueno aff
Merve Gizem Bozkurt, Dante Fratta, William J. Likos

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersUniversity of Wisconsin-Madison
KeywordsCapillary actionMaterials scienceGeotechnical engineeringWettingMeniscusParticle (ecology)Composite materialMechanicsDeformation (meteorology)GeologyGeometry

Abstract

fetched live from OpenAlex

The mechanical response of near-surface unsaturated soils in large-strain environments such as earthquakes, landslides or debris flows is highly dependent on capillary forces. While the evolution of capillary forces under static loading has been studied in detail, the dynamic response of unsaturated soils associated with the viscous deformation and rupture of interparticle liquid menisci at large strains is not as well characterized. Particle-scale pullout tests were conducted to achieve better understanding of how separation rates and distances contribute to capillary force evolution and meniscus rupture between two equally sized glass spheres. Capillary forces evolve nonmonotonically in a manner that first increases and then decreases with increasing separation distances and is dependent on the initial meniscus geometry and wettability of the particles. The rate of capillary force reduction and particle separation distance at liquid bridge rupture are functions of the meniscus volume and rate of particle separation. The two-particle experimental results suggest that the dynamic response of bulk (multi-particle) unsaturated soil systems would depend on processes of drainage and imbibition and provide insight into the evolution of stiffness and the ductility of unsaturated soils undergoing large-strain deformations.

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 score0.469
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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