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Record W2606520071 · doi:10.1139/cgj-2016-0507

Membrane filter properties and application of the filter to undrained cyclic triaxial test of unsaturated materials

2017· article· en· W2606520071 on OpenAlexvenueno aff
Hailong Wang, Junichi Koseki, Tomoyoshi Nishimura, Yukika Miyashita

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceFilter (signal processing)MembraneGeotechnical engineeringHydraulic conductivityFiltration (mathematics)SuctionPore water pressureFilter cakeComposite materialRepeatabilityCeramicDiffusionTriaxial shear testSoil waterGeologyChemistryEngineeringShear (geology)ChromatographySoil scienceMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Properties of the membrane filter recently introduced as an alternative to the ceramic disk are revealed through diffusion and hydraulic conductivity tests. It is shown that diffusion of air through the membrane filter is significantly affected by suction magnitude and that hydraulic conductivity of the membrane filter can easily be affected by the quality of water used in the test. The application of the membrane filter to the soil-water characteristic curve tests (SWCC tests) shows that similar SWCCs can be obtained by employing pressure plate apparatuses with either the ceramic disks or the membrane filter installed, and that repeatability of the SWCC by using the membrane filter pressure plate apparatus is reasonably good. The application of the membrane filter to the undrained cyclic loading test of unsaturated sandy materials shows that the response (the duration to measure the equilibrated pore-water pressure of unsaturated materials) of the membrane filter pedestal in a modified triaxial system may be as short as ∼2 s in certain test conditions, and fairly good pore-water pressure and air pressure measurements can be obtained during undrained cyclic loading with a loading frequency of 0.1 Hz.

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 score0.075
Threshold uncertainty score0.506

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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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