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Record W4402651417 · doi:10.1051/e3sconf/202456919004

Integration of geosynthetics in reclaiming an oil sands tailings pond

2024· article· en· W4402651417 on OpenAlexaff
Ying Zhang, Assile Aboudiab, Ayman H. Abusaid, Gordon W. Pollock, Derek Uffen

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsSuncor Energy (Canada)Wave Control Systems (Canada)Workers Compensation Board of Alberta
Fundersnot available
KeywordsTailingsGeosyntheticsOil sandsGeologyMining engineeringEnvironmental scienceGeotechnical engineeringArchaeologyGeographyAsphaltMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Global mining operations produce significant amounts of soil waste referred to as tailings. In oil sands mining, a portion of the deposited tailings is extremely soft and is primarily in a fluid state with solid contents (by weight) ranging between 30% and 40%. As part of reclaiming its Pond 5 oil sands tailings pond, Suncor constructed an engineered “floating” cover on top of the soft tailings deposits between 2010 and 2017 over an area spanning approximately 200 hectares to provide trafficability for limited construction equipment. This initial cover consisted of two layers of geosynthetics overlain by 2 m of petroleum coke. After construction of the initial cover, Vertical Strip Drains (VSDs) were installed through the majority of the capped area to enhance the consolidation rate of the underlying soft tailings. Additional coke has been placed on top of the initial coke cover to a total thickness of 4 m to 6 m. Previous publications have discussed the cover design, installation details of the geosynthetics, installation and performance of the VSDs, and performance of the cover. This paper presents the geosynthetics design, inspection of the geosynthetics post cover construction, and an update on the performance of the coke cover.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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
Published2024
Admission routes1
Has abstractyes

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