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Record W2066531407 · doi:10.1139/t99-120

Hydraulic conductivity of bentonite-sand mixtures

2000· article· en· W2066531407 on OpenAlexvenueno aff
P. V. Sivapullaiah, A. Sridharan, V. K. Stalin

Bibliographic record

VenueCanadian Geotechnical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic conductivityBentoniteVoid ratioGeotechnical engineeringAtterberg limitsConductivityWater contentPorositySoil waterMaterials sciencePermeability (electromagnetism)Soil scienceGeologyChemistry

Abstract

fetched live from OpenAlex

The use of bentonite alone or amended with natural soils for construction of liners for water-retention and waste-containment facilities is very common. The importance of bentonite content in reducing the hydraulic conductivity of liners is well recognised. The study illustrates the role of the size of the coarser fraction in controlling the hydraulic conductivity of the clay liner. It has been shown that at low bentonite contents the hydraulic conductivity of the liner varies depending on the size of the coarser fraction apart from clay content. At a given clay content, the hydraulic conductivity increases with an increase in the size of the coarser fraction. But when the clay content is more than that which can be accommodated within the voids of the coarser fractions, the hydraulic conductivity is controlled primarily by clay content alone. Four different methods of predicting hydraulic conductivity of the liners are presented. Using two constants, related to the liquid limit, the hydraulic conductivity can be predicted at any void ratio.Key words: clays, hydraulic conductivity, liquid limit, liners, void ratio.

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)0.000
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.198
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 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

Citations197
Published2000
Admission routes1
Has abstractyes

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