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Record W1980985356 · doi:10.1520/gtj101373

New Slurry Displacement Method for Reconstitution of Highly Gap-Graded Specimens for Laboratory Element Testing

2008· article· en· W1980985356 on OpenAlexaff
Ali Khalili, Dharma Wijewickreme

Bibliographic record

VenueGeotechnical Testing Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSlurryDisplacement (psychology)Geotechnical engineeringMaterials scienceShear (geology)Laboratory testTailingsDirect shear testHomogeneousSoil testSoil waterGeologyComposite materialEngineeringMetallurgySoil scienceMathematics

Abstract

fetched live from OpenAlex

Abstract A new “slurry displacement” method was developed for reconstitution of cylindrical specimens of highly gap-graded soils for laboratory element testing. The method stems from a need to conduct laboratory element tests on mixtures of waste rock and tailings with specific relevance to the development of new technology and material science for mining industry. The slurry displacement method allows preparing uniform/homogeneous specimens of highly gap-graded materials in a saturated condition, thus overcoming the difficulties in the use of currently available specimen preparation techniques. The suitability of the technique to replicate specimens is demonstrated by the repeatable test results obtained from shear testing of identical specimens prepared using the method.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.088
GPT teacher head0.320
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

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