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Record W4393981716 · doi:10.36487/acg_repo/2455_22

Analysing the segregation of coarse tailings particles with a zone-formation differential settling model

2024· article· en· W4393981716 on OpenAlexaff
Y Li, Dirk van Zyl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTailingsSettlingDifferential (mechanical device)GeologyEnvironmental scienceMaterials scienceEnvironmental engineeringPhysicsThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

This study delves into the segregation and settling behaviours of tailings suspensions with varying initial solids contents, with a specific focus on the initial segregation process. Conventional and interruptive batch settling tests were performed to evaluate the hindered settling process of copper tailings. During these tests, particular attention was paid to the differential settling behaviour at the initial stage, during which coarse particles segregated from the suspension and accumulated at the bottom. The particle size distribution profile of the suspension was analysed in detail, revealing that segregation was a prevalent phenomenon in all settling tests. To perform a theoretical analysis of this segregation behaviour, a zone-formation differential settling model was introduced and applied, allowing a detailed discussion of the settling behaviour of individual particle species. Consequently, the findings offer insights into the variation of the segregation behaviour and formation of the sediment under different initial conditions. Suggestions are also provided for the application of the zone-differential settling model on the sedimentation of tailings which contains fine particle species.

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 score0.304
Threshold uncertainty score0.179

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 head0.194
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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