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Record W3207307290 · doi:10.1155/2021/7561977

Shear Properties of Cemented Paste Backfill under Low Confining Stress

2021· article· en· W3207307290 on OpenAlexafffund
Andrew Pan, Murray Grabinsky, Lijie Guo

Bibliographic record

VenueAdvances in Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoBarrick Gold Corporation
KeywordsMaterials scienceComposite materialCuring (chemistry)Shear (geology)Shear stressGeotechnical engineeringCementation (geology)Direct shear testTriaxial shear testCementGeology

Abstract

fetched live from OpenAlex

Cemented paste backfill (CPB) plays an important role in the mining industry due to safety, cost efficiency, and environmental benefits. Studies on CPB have improved the design and application of paste backfill in underground mines. Direct shear is one of the most fundamental parameters for assessing backfill strength. This study harnesses direct shear tests to explore the low confining stress behavior of CPB. We perform all the tests in a standard apparatus on the combination of three binder contents of 4.2%, 6.9%, and 9.7% CPB with four curing times of 3, 7, 14, and 28 days, respectively. The applied confining stress levels vary in a range according to the in situ regime. Results are presented by strength envelope, stress‐strain property, and shear strength with curing time and binder content. The data suggest that the shear strength follows the Mohr–Coulomb envelope in which the shear strength and behavior are time and binder content dependent. In addition, the results show that shear strength is strongly related to the binder content than the curing time, namely, the higher the degree of binder hydration, the higher the cementation binding force between CPBs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.191
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes2
Has abstractyes

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Same venueAdvances in Civil EngineeringSame topicTailings Management and PropertiesFrench-language works237,207