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Record W2121534667 · doi:10.1139/t07-091

Water retention curve and hydraulic conductivity function of highly compressible materials

2007· article· en· W2121534667 on OpenAlexaffvenueabout
Sérge-Étienne Parent, Alexandre R. Cabral, Jorge G. Zornberg

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHydraulic conductivityVoid ratioConsolidation (business)CompressibilityGeotechnical engineeringVoid (composites)SuctionWater retention curvePorosityMaterials scienceWater retentionGeologyEnvironmental scienceSoil scienceMechanicsSoil waterComposite materialEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

A model capable of describing the suction-induced consolidation curve (void ratio function) and water retention curve (WRC) of highly compressible materials (HCM) is developed, validated, and finally applied to describe the WRC of deinking by-products (DBP). DBP are a highly compressible by-product of paper recycling used in geoenvironmental applications. Validation is conducted by modelling the WRC and the void ratio function for a well documented silty sand from Saskatchewan, Canada. The WRC and void ratio function were used to predict its hydraulic conductivity function (k-function). The water content, suction, and volumetric deformation data of DBP are obtained using an experimental technique that allows determination of the WRCs of HCMs that is suitable for prediction of the DBP k-function. The results show that volumetric water contents are underestimated if volume changes are not accounted for, leading to inaccuracies in the WRCs, thus inaccurately predicted k-functions. It is shown that the newly developed model is better suited for HCMs than currently available models, in particular for HCMs that continue to undergo significant volume changes when the applied suction exceeds the air-entry value.

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 score0.093
Threshold uncertainty score0.329

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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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

Citations20
Published2007
Admission routes3
Has abstractyes

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