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Record W4224225135 · doi:10.1520/gtj20210235

Determining Soil Plasticity Utilizing Manafi Method and Apparatus

2022· article· en· W4224225135 on OpenAlexaff
Masoud S. G. Manafi, An Deng, Abbas Taheri, Mark B. Jaksa, H. B. Nagaraj

Bibliographic record

VenueGeotechnical Testing Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsQueen's University
Fundersnot available
KeywordsPlasticityAtterberg limitsGeotechnical engineeringSoil waterSoil testPermeability (electromagnetism)CompressibilityMaterials scienceEnvironmental scienceSoil scienceGeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Soil plasticity is one of the essential index properties required for classifying soils in geotechnical engineering practice. Determination of plasticity properties of soils is also critical for correlation with their engineering properties such as shear strength, permeability, and compressibility. However, present standard test methods for soil plasticity suffer, to different extents, from operator-dependency and inconsistency. This study introduces a new technique utilizing a combined qualitative and quantitative approach for soil property determinations, namely the Manafi Method and Apparatus. The method is proposed as an alternative technique to determine the liquid and plastic limits of soils. The proposed technique is instrumented with a new soil extrusion device to quantify the workability of soils and is calibrated to translate the workability to their liquid and plastic limits. The method is applied to seven soils of varying particle sizes and plasticity to determine the liquid and plastic limits, and the results are compared with those obtained by the conventional methods. The outcomes suggest that the new technique provides a more precise and reliable means of soil plasticity determination in the studied samples.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.002
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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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