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Record W2464016183 · doi:10.1139/cgj-2015-0343

Laboratory assessment of the effects of polymer treatment on geotechnical properties of low-plasticity soil slurry

2016· article· en· W2464016183 on OpenAlexaffvenue
David Reid, Andy Fourie

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsGolder Associates (Canada)
FundersAustralian Centre for Geomechanics
KeywordsGeotechnical engineeringConsolidation (business)Void ratioMaterials scienceSlurryPlasticityMaterial propertiesComposite materialHydraulic conductivityGeologySoil waterSoil science

Abstract

fetched live from OpenAlex

A laboratory study was undertaken to assess the effects of polymer treatment (PT) on the geotechnical behaviour of a low-plasticity slurry. PT was undertaken on the material using a high molecular weight polyacrylamide polymer. Slurry consolidometer and triaxial testing were undertaken to compare the geotechnical behaviour of the material following PT, compared to an untreated (UT) condition. The material was prepared from a slurry state in a nonsegregating condition, to enable comparison of uniform samples. Testing indicated that PT resulted in lower final consolidated densities at a given magnitude of effective stress, while increasing the rate of consolidation and hydraulic conductivity. Triaxial testing indicated that PT material, when sheared, appeared to exhibit a different critical state line (CSL) than UT material. The PT CSL was higher in void ratio terms than the UT CSL, and with an increased compressibility. Both PT and UT material exhibited undrained shear strengths consistent with the state of the specimen with respect to that material’s CSL. PT material exhibited higher undrained shear strengths at a given density. All the effects observed following PT appeared to persist across a wide range of effective stresses.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.487

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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 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

Citations21
Published2016
Admission routes2
Has abstractyes

Explore more

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