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Record W4367155062 · doi:10.36487/acg_repo/2355_62

A comparison between in situ techniques to measure undrained shear strength of oil sands tailings

2023· article· en· W4367155062 on OpenAlexaffabout
Iman Entezari, Dallas McGowan, Joseph Glavina, Jamie Sharp

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBanff CentreGeomechanica (Canada)University of Alberta
Fundersnot available
KeywordsTailingsOil sandsGeologyShear strength (soil)Geotechnical engineeringShear (geology)In situMaterials sciencePetrologySoil scienceComposite materialChemistryMetallurgyAsphalt

Abstract

fetched live from OpenAlex

A comprehensive comparison between undrained shear strength (Su) measured from piezocone penetration tests (CPTu), piezoball penetration tests (BCPTu), and electronic field vane shear tests (eVST) in soft tailings is presented. To evaluate the comparability of Su from both penetrometers and eVST, a comparative study was performed using data collected from oil sands tailings storage facilities in northern Alberta, Canada. Two paired datasets of eVST-CPTu and eVST-BCPTu were compiled and the relationships between Su values from the three strength measurement techniques were explored. Results show that Nkt and Nball of 15 and 12.2 are reasonable values to scale net tip resistance and determine the strength in soft tailings from CPTu and BCPTu, respectively. Furthermore, comparing to eVST results, both CPTu and BCPTu penetrometers were found to be effective tools in profiling the strength of soft tailings when Su is less than 10 kPa. BCPTu was observed to be slightly more accurate than CPTu in very low strength fluid-like 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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