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Record W2964146051 · doi:10.1139/cgj-2019-0271

Field and laboratory behaviour of fine-grained soil stabilized with lime

2019· article· en· W2964146051 on OpenAlexvenueno aff
Nilo César Consoli, Eduardo Bittar, Rubén Alejandro Quiñónez Samaniego, Hugo Carlos Scheuermann Filho, Nuno Cristelo

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoMinisterio de Economía y CompetitividadCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLimeGeotechnical engineeringShrinkageSoil waterDeflection (physics)Clay soilPorositySoil stabilizationCompressibilityService lifeEnvironmental scienceWater contentCuring (chemistry)Materials scienceGeologySoil scienceEngineeringComposite materialMetallurgyPhysics

Abstract

fetched live from OpenAlex

Fine-grained soils, due to their high plasticity, possess great shrinkage potential and high compressibility and are responsible for very substantial maintenance costs during the service life of the associated infrastructures. Stabilization of such soils with lime is one of the most effective procedures to mitigate these undesirable characteristics and, at the same time, to enhance their mechanical properties. Current research seeks, through field and laboratory tests, to quantify the influence of calcitic hydrated lime on the strength and deflection characteristics clayey soil from the Paraguayan region of Chaco. The influence of different dry unit weights, lime amounts, and curing periods on the strength and deflection of a Paraguayan clay stabilized with lime was assessed. The present work shows, for the first time ever, that the porosity/lime index is the proper parameter to be used in the field when dealing with the strength of clay–lime bases of pavements. Hence, the results presented herein are a contribution to understanding the conditions in which these soils can be stabilized to be used in infrastructural applications.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.466

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 integrity0.0000.001
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 head0.004
GPT teacher head0.172
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 designObservational
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

Citations22
Published2019
Admission routes1
Has abstractyes

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