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Record W2562536730 · doi:10.1680/jgele.16.00099

The role of particle type on suffusion and suffosion

2016· article· en· W2562536730 on OpenAlexaff
P. Slangen, R. J. Fannin

Bibliographic record

VenueGéotechnique Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsUniversity of British ColumbiaBGC Engineering (Canada)
Fundersnot available
KeywordsPermeameterParticle (ecology)BeadMaterials scienceComposite materialInternal erosionGeotechnical engineeringSoil waterGeologyHydraulic conductivity

Abstract

fetched live from OpenAlex

A distinction can usefully be made between suffusion, which describes the removal of fine particles by seepage flow from a body of soil without volume change, and suffosion, which is characterised by the removal of fine particles, accompanied by contractive volume change. Four pairs of gap-graded specimens were prepared, each pair comprising one glass bead specimen and one soil specimen with sub-angular particles, with nominally identical particle size distributions. The reconstituted specimens were isotropically consolidated to the same confining stress, and then subject to multi-stage upward seepage flow in a flexible wall permeameter. The glass beads and soil specimens with a finer fraction content of 0·20 exhibited suffusion. It appears that the susceptibility to suffusion of these specimens was not governed by particle type. Suffosion was evident in glass bead specimens with a finer fraction content of 0·35, but it was not triggered at an equal, or even larger, hydraulic gradient in soil specimens with nearly identical gradations. Particle type thus appears to be a factor governing the susceptibility to suffosion and should be considered when investigating this phenomenon.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.162
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations32
Published2016
Admission routes1
Has abstractyes

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