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Record W2600013803 · doi:10.1061/9780784480472.029

Evaluation of Geotextile Performance for the Filtration of Fine-Grained Tailings

2017· article· en· W2600013803 on OpenAlexafffund
Patricia I. Dolez, Éric Blond

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsCTT Group (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaShell CanadaCanadian Natural Resources Limited
KeywordsGeotextileTailingsCloggingFiltration (mathematics)Geotechnical engineeringOil sandsHydraulic conductivityEnvironmental scienceMaterials scienceGeologyComposite materialAsphaltMetallurgySoil scienceSoil water

Abstract

fetched live from OpenAlex

Geotextile filters are an interesting solution to dewater tailings as they can be installed on existing ponds or be deployed during operation. However, filtration of the fine-grained tailings that are encountered in the oil sands and other mining industries raises several issues: blinding or clogging of the geotextile by the fine particles, or piping. Since the hydraulic conductivity of fine-grained tailings is generally too low for the ASTM D5101 standard test method for soil/geotextile system filtration compatibility, a test setup has been specifically developed and used to study the filtration behavior of needle-punched and heat-bonded nonwovens with various types of oil sands tailings. The permittivity of the geotextile/tailings systems was always several orders of magnitude lower than the water permittivity of the geotextile. On the other hand, no clogging or blinding was observed over more than 100 days. These results show that geotextiles offer great promise for the filtration of fine-grained tailings.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.520
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueGeotechnical Frontiers 2017Same topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207