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Record W2051804611 · doi:10.1680/geng.2002.155.4.253

Influence of preparation techniques on the index properties of clay

2002· article· en· W2051804611 on OpenAlexfundno aff
T. Navaneethan, V. Sivakumar

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilUniversity of Ottawa
KeywordsAtterberg limitsGranular materialParticle-size distributionMixing (physics)Geotechnical engineeringMineralogyParticle sizeMaterials scienceComposite materialGeologyWater contentChemical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Liquid limit (LL) and plastic limit (PL) are frequently used as indicators of the behaviour of fine-grained materials. These parameters are a prerequisite in every geotechnical investigation. Careful and consistent material preparation is a key component in the laboratory procedures adopted to determine the LL and PL. In preparing samples for such tests, various techniques are used in practice, and this paper examines the influence of these techniques on the actual magnitude of LL and PL measured. The index properties of a glaciolacustrine deposit, locally known as Belfast Upper Boulder Clay, which was laid down in glacial Lake Belfast, are examined. The index properties were determined on samples of the material prepared in different ways, including drying the material at two different temperatures (40°C and 110°C), crushing the dried material from coarse granular to fine granular form by adopting varying degrees of crushing effort, and mixing the dry material with both deionised water and tap water. The results indicate that the crushing procedure has a significant influence on particle size distribution and a consequential effect on the LL and PL. Pore water chemistry and the drying procedure also have a measurable influence on the magnitude of LL and PL.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.179
Teacher spread0.169 · 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

Citations8
Published2002
Admission routes1
Has abstractyes

Explore more

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