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Record W2335688768 · doi:10.1061/9780784412121.428

The Use of Waste Rock Inclusions to Improve the Seismic Stability of Tailings Impoundments

2012· article· en· W2335688768 on OpenAlexaff
Michael B. James, Michel Aubertin

Bibliographic record

VenueGeoCongress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTailingsLiquefactionGeotechnical engineeringGeologyTailings damSlurryMining engineeringEnvironmental scienceMaterials science

Abstract

fetched live from OpenAlex

Tailings are typically deposited as slurry over an extended period of time. Many types of tailings, such as those from hard rock mines, are fine-grained and cohesionless, making them particularly susceptible to liquefaction. The primary effects of earthquake shaking on tailings impoundments include horizontal loading on the retaining dykes and the development of excess porewater pressures (which may lead to liquefaction) in the retained tailings. Secondary effects can include additional horizontal loading on the dykes due to the strength loss in the tailings and seepage pressures in the dykes due to the dissipation of porewater pressures following shaking. The placement of waste rock within a tailings impoundment to create inclusions of more rigid material could improve the seismic stability of tailings impoundments by providing reinforcement against deformation of the impoundment. Such inclusions can also help dissipate porewater pressures after shaking. The paper presents numerical analyses of a reference tailings impoundment, with and without waste rock inclusions, subjected to a seismic loading and an evaluation of the effects of the inclusions on the dynamic performance of the tailings impoundment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.380

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)0.0000.000

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 head0.015
GPT teacher head0.221
Teacher spread0.205 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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