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Record W4220998336 · doi:10.1139/cgj-2021-0191

A 3D visualization method for identifying fabric characteristics during suffusion using transparent soil

2022· article· en· W4220998336 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersGraduate School, Chongqing UniversityResearch and Innovation FoundationNatural Science Foundation of ChongqingNational Natural Science Foundation of China
KeywordsInternal erosionIntergranular corrosionCloggingGeotechnical engineeringMaterials scienceParticle (ecology)Particle flowPorositySoil waterGeologyComposite materialMechanicsSoil scienceDiscrete element methodMicrostructure

Abstract

fetched live from OpenAlex

Suffusion is a process by which fine particles move through the voids between coarse particles (intergranular voids) by seepage flow. Therefore, identifying the arrangement of coarse particles (coarse matrix), intergranular voids, and the spatial distribution of fine particles is a key issue for studying suffusion. Transparent soil is being increasingly used to replace real soil in laboratory model tests for studying the internal particle movements and pore fluid flow in soils. This study established an experimental setup for scanning a transparent soil specimen during seepage by a moving laser and made an attempt to construct a 3D digital mesostructural model of the specimen based on the images of the sequentially illuminated cross-sections. An example test on a gap-graded soil was conducted to show the performance of the presented method. It demonstrated that the coarse matrix and intergranular voids can be visualized and quantified at satisfactory precisions; moreover, the loss and redistribution of fine particles as well as important phenomena such as deposition at pore bodies and clogging of pore throats can also be visualized and identified qualitatively.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.280
Teacher spread0.248 · 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