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Record W2771343713 · doi:10.1016/j.ijmst.2017.12.007

A framework for coupled consolidation-desiccation behaviour of clay slurries

2017· article· en· W2771343713 on OpenAlexafffund
Faseel S. Khan, Shahid Azam

Bibliographic record

VenueInternational Journal of Mining Science and Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsSlurryConsolidation (business)DesiccationGeotechnical engineeringEnvironmental scienceGeologyEnvironmental engineeringBusinessBotanyBiologyAccounting

Abstract

fetched live from OpenAlex

A framework is proposed to understand coupled consolidation-desiccation in clay slurries using effective stress and soil suction as state variables. Constitutive surfaces were derived from best-fit equations for reference curves where one state variable was kept at 1 kPa and the other was varied up to 106 kPa. Results indicated that volume compressibility comprises of apparent pre-consolidation up to 100 kPa beyond which a single best-fit curve is valid. Similarly, the theoretical virgin compression line (specific gravity dependant), indicated that both state variables have the same effect up to the air entry value (1000 kPa at a void ratio of 1.8) beyond which void ratio reduces more readily under effective stress than under soil suction. Likewise, hydraulic conductivity remained saturated up to the air entry value and varied from 10−9 m/s to 10−12 m/s. Finally, the unsaturated hydraulic conductivity correlated well with empirical relationship up to 10−14 m/s beyond which vapor flow became dominant.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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 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

Citations3
Published2017
Admission routes2
Has abstractyes

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