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Record W2109160689 · doi:10.1139/t10-083

Seepage, leaching, and embankment instability

2011· article· en· W2109160689 on OpenAlexaffvenueabout
Alex Man, Jim Graham, James Blatz

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsAtomic Energy (Canada)University of Manitoba
Fundersnot available
KeywordsGeotechnical engineeringDredgingGeologySofteningLeveeCementation (geology)Leaching (pedology)SiltBrittlenessInstabilityConsolidation (business)Soil waterMaterials scienceSoil scienceGeomorphologyMetallurgyCementComposite material

Abstract

fetched live from OpenAlex

Water-retention dykes at a hydroelectric generating station in southeastern Manitoba experienced irregular instabilities over many years after they were heightened in the late 1940s. Samples from an unstable section of dyke showed considerable reductions in gypsum content and increased brittleness compared with those of neighbouring unloaded clay. The samples also showed that lowering the content of gypsum reduced the sizes of yield loci and the strains required for strain softening to occur. These observations and the time-dependent nature of the problem suggested that seepage from the forebay had leached naturally occurring cementation from the foundation clay and subsequently changed its behaviour. Stress–deformation modeling indicated the foundation clay had yielded during initial construction and again when the dykes were heightened. Seepage from the forebay occurred at different rates in different locations through the irregular sand–silt partings that are generally present in proglacial clays. Leaching and strain softening were therefore also irregular. Leaching led to reduction in the size of the yield loci and therefore increased the volume of soil that yielded under loading from the dyke. In slope-stability modeling, excess pore-water pressures arising from time-dependent brittleness were sufficient to cause instability of the dykes.

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 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: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.653

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.177
Teacher spread0.165 · 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.

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

Citations6
Published2011
Admission routes3
Has abstractyes

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