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Record W1741027205 · doi:10.1139/cgj-2014-0156

Determination of the tensile strength of unsaturated tailings using bending tests

2015· article· en· W1741027205 on OpenAlexafffundvenue
Bibiana Narvaez, Michel Aubertin, Faustin Saleh-Mbemba

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsCohesion (chemistry)Ultimate tensile strengthGeotechnical engineeringBrittlenessSaturation (graph theory)Materials scienceBendingWater retentionDegree of saturationSuctionFriction angleTension (geology)Soil waterComposite materialGeologyMathematicsMetallurgySoil scienceChemistryEngineering

Abstract

fetched live from OpenAlex

Bending tests were conducted on specimens of unsaturated tailings from three hard rock mines to evaluate their tensile strength. Saturated samples were prepared at an initial water content, w 0 , of 40% and then naturally dried under ambient conditions to pre-selected degrees of saturation, S r , which can be related to the corresponding suction using the water retention curve. The basic interpretation of the bending tests results is based on an elastic–brittle behavior. The results show how the tensile strength, σ t , of unsaturated tailings varies with water content, w (and S r ). The experimental data are also used to evaluate Young’s modulus in tension, E t , and to estimate the apparent cohesion, c app , as a function of S r . Predictive equations are also applied to estimate the values of σ t of unsaturated tailings using the water retention curve.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.358

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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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

Citations33
Published2015
Admission routes3
Has abstractyes

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