MétaCan
Menu
Back to cohort
Record W3099238177 · doi:10.1139/cgj-2020-0372

Physical and numerical modelling of drainage and consolidation of tailings near a vertical waste rock inclusion

2020· article· en· W3099238177 on OpenAlexaffvenue
Faustin Saleh-Mbemba, Michel Aubertin

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsPolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsTailingsConsolidation (business)DrainageGeotechnical engineeringGeologyPore water pressureSlurryInclusion (mineral)Permeability (electromagnetism)Mining engineeringEnvironmental scienceEnvironmental engineeringMineralogyMaterials science

Abstract

fetched live from OpenAlex

The use of waste rock inclusions in tailings impoundments is a recent technique that offers many advantages, but it also raises a few technical issues that must be addressed to optimize their design. A laboratory physical modelling study was conducted to assess the effect of waste rock inclusion on the behavior of initially saturated hard rock mine tailings in terms of drainage and consolidation. The evolution of pore-water pressures and settlements after hydraulic deposition of the fine-grained tailings (slurry), with and without a drainage inclusion, has been monitored and analysed. This investigation also focused on the evolution of the tailings void ratio and volumetric water content, the amount of water transferred to the waste rock, and the movement of fine particles at the interface between the two materials. The experimental results are used to demonstrate how such waste rock inclusion can affect tailings consolidation by reducing pore-water pressures with accelerated water drainage, for various imposed conditions. The experimental data are also analysed with numerical models to better understand the coupled processes involved. A discussion follows on practical implications of the use of waste rock inclusions in tailings impoundments.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.373

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 head0.014
GPT teacher head0.191
Teacher spread0.177 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicTailings Management and PropertiesFrench-language works237,207