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Record W2052068593 · doi:10.1080/1064119x.2013.848252

Geotechnical and Rheological Characteristics of Saguenay Fjord Sediments Near the Transition from Solid to Liquid

2013· article· en· W2052068593 on OpenAlexafffundabout
Sueng Won Jeong, Jacques Locat, Serge Leroueil

Bibliographic record

VenueMarine Georesources and Geotechnology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsCegep de Sainte FoyUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaKorea Institute of Geoscience and Mineral Resources
KeywordsGeologyGeotechnical engineeringRheologyFjordViscometerShear (geology)ViscosityGeomorphologyPetrologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This paper examines the geotechnical and rheological characteristics in terms of the transition from slide to flow in submarine landslides. This paper contains the results of a series of laboratory tests on the Saguenay Fjord fine-grained sediments. There are two types of tests: (1) vane shear tests in which intact and remolded samples are sheared and exposed to ambient water and (2) rheological tests performed on remolded sediment (with variable liquidity index) using a viscometer. The results explain the effect of water infiltration into soil. The sudden reduction in shear strength varied between about 300 and 800 Pa (Δw = 2–20%). The viscosity obtained from normalized flow curves (i.e., the logarithmic plot of shear stress versus the shear rate) were employed to characterize the rheology of soil. The mean value of viscosity (strength parameter in this case) as a function of shear rate from the results of fine-grained sediments varied between about 0.1 and 0.4. The values of yield stress and viscosity that were associated with the passage from the remolded to the fluidized state were in the range of 200 to 300 for Saguenay Fjord sediments.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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 designObservational
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

Citations12
Published2013
Admission routes3
Has abstractyes

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