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Record W2019140295 · doi:10.1139/t09-116

Modelling capacity of transparent soil

2010· article· en· W2019140295 on OpenAlexaffvenue
Jinyuan Liu, Magued Iskander

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGeotechnical engineeringDigital image correlationSpeckle patternDisplacement (psychology)Materials scienceDisplacement fieldDeformation (meteorology)OpticsGeologyEngineeringComposite materialStructural engineeringPhysics

Abstract

fetched live from OpenAlex

The modelling capacity of transparent soil was investigated by comparing boundary soil displacement fields under a model footing in a transparent soil model with those from a natural soil model. Transparent soil is made of either amorphous silica gels or powders and a pore fluid with a matching refractive index to model sand and clay. An optical system consisting of a laser light, camera, frame grabber, and computer was developed to optically slice a transparent soil model. A distinctive laser speckle pattern was generated by the interaction between the laser light and transparent soil. Two laser speckle images before and after a deformation were used to calculate the displacement field using an image processing technique called digital image correlation. The comparative study shows that transparent soil can be used to study natural soil with some limitations. Transparent soil and the developed optical system could be used to explore opportunities for more advanced nonintrusive three-dimensional deformation measurements for various soil–structure interaction problems.

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 score0.052
Threshold uncertainty score0.877

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 integrity0.0000.001
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 head0.196
Teacher spread0.174 · 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

Citations109
Published2010
Admission routes2
Has abstractyes

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