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Record W2417685600 · doi:10.1139/cgj-2015-0585

Thermal conductivity of geosynthetic clay liners

2016· article· en· W2417685600 on OpenAlexaffvenue
Mohammad Ali, Abdelmalek Bouazza, Rao Martand Singh, Will P. Gates, R. Kerry Rowe

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersAustralian Research Council
KeywordsGeosynthetic clay linerBentoniteThermal conductivityGravimetric analysisMaterials scienceComposite materialThermogravimetric analysisGeotechnical engineeringGeotextileWater contentGeosyntheticsHydraulic conductivitySoil waterGeologyChemistrySoil science

Abstract

fetched live from OpenAlex

The thermal conductivities of powdered and granular bentonite based needle punched geosynthetic clay liners (GCLs) were investigated at different gravimetric water contents under 25, 50, 75, and 100 kPa vertical stresses. Both types of GCLs exhibited an increase in thermal conductivity with increasing vertical stress at all water contents. The effect of vertical stresses was more pronounced for the specimens hydrated at lower gravimetric water contents and this was attributed to their high initial volumetric air content. The variability of water distribution in partially hydrated GCLs has been identified as a factor that may affect their thermal conductivity. The forms of bentonites (i.e., powder or granular) affected their thermal conductivities; however, this effect was less apparent at higher gravimetric water contents due to the reduced air content and gel formation in the bentonites. Finally, the GCL thermal conductivity calculated from the measured thermal conductivities of its various constituents (i.e geotextile and bentonite) components differed from the measured values. This was attributed to the nonuniform water distribution across the GCL specimen and change in material properties when components of GCL were disassembled.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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.

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

Citations45
Published2016
Admission routes2
Has abstractyes

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