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Record W4220858709 · doi:10.1061/9780784484012.017

Impact of Colloidal Silica Treatment on an Earthfill Dam

2022· article· en· W4220858709 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsGeotechnical engineeringEarthworksOutflowEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

The impact of the use of sand treated with Colloidal Silica (CS) on a model 80 m high-earthfill dam was assessed through numerical modelling. The hydro-mechanical properties of the CS-treated soil were taken from a previous laboratory study. Different strategies of treatment placement were studied, with increasing volumetric fractions of the dam being replaced by the treated material. Staged construction of the earthwork was simulated to evaluate the horizontal and vertical displacements at critical points within the dam. Different kinematics have been observed, depending on the quantities of CS-treated material included in the soil structure. Moreover, the use of the treated soil was beneficial in terms of water outflow and upstream slope stability, in correspondence with different water reservoir levels. Different strategic patterns of CS-treated sand placement are proposed herein to fully exploit the storage capacity of the dam, optimizing its stability.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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