MétaCan
Menu
Back to cohort
Record W4225376179 · doi:10.1139/cgj-2021-0585

Synergistic effects of density, gradation, particle size, and particle shape on the water entry pressure of hydrophobized sands

2022· article· en· W4225376179 on OpenAlexvenueno aff
Xin Xing, Yunesh Saulick, Sérgio D. N. Lourenço

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGradationParticle sizeParticle (ecology)Materials scienceGeotechnical engineeringQuartzComposite materialInfiltration (HVAC)MineralogyChemistryGeology

Abstract

fetched live from OpenAlex

This paper examines the specific role and interdependencies between soil density, particle size, particle shape, and gradation on the water entry pressure of hydrophobized sands. The tested granular materials include quartz sands of three size ranges, glass beads, and crushed glass. All granular materials were initially silanized with polydimethylsiloxane (PDMS) coatings to achieve the same intrinsic hydrophobicity. The water-ponding method was adopted to measure the water entry pressure in a comprehensive parametric study that consisted of 96 tests. Despite an acute sensitivity of the water entry pressure to relative density, the results revealed a more dominant effect of particle size. The largest water entry pressure (hydrophobized fine sand) was 14 times that of the lowest (hydrophobized coarse sand). Relatively higher water entry pressures were recorded with sands having larger coefficients of uniformity. A comparison between hydrophobized glass beads and crushed glass also revealed that rounded particles were less effective in retarding water infiltration in a dense state. A relationship, based on Jurin's law, was proposed for dense sands whereby the water entry pressure is obtained from the coefficient of uniformity, mean particle size, and particle shape.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.267

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 head0.004
GPT teacher head0.164
Teacher spread0.159 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207