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Assessment of self-weight consolidation of flocculated fluid fine tailings under various environmental conditions

2018· article· en· W2804936591 on OpenAlexafffund
Bereket Fisseha, G WILSON, Paul Simms

Bibliographic record

VenuePaste/˜Pœaste · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsConsolidation (business)Environmental scienceMaterials scienceBusinessMetallurgy

Abstract

fetched live from OpenAlex

Self-weight consolidation properties of polymer-flocculated fluid fine tailings (FFT) have been investigated in a laboratory using a meso-scale column apparatus with dimensions of 300 mm (diameter) by 1.8 m (height). Four meso-scale columns were used to carry out self-weight consolidation of flocculated FFT with flocculant dosages of 650 g/L (one column) and 850 g/L (three columns). The laboratory investigation attempts to mimic set-ups of the meso-scale consolidation columns under various environmental considerations such as with no evaporation (one column of 850 g/L dosage), with evaporation only (one column of 650 g/L and one column of 850 g/L dosage), and with evaporation and decantation (one column of 850 g/L dosage). The laboratory investigation enables the characterisation of self-weight consolidation of flocculated FFT, using the relationship between the total settlement of deposited tailings versus measured excess pore water pressure dissipation and expected hydrostatic line for various flocculant dosages and environmental considerations. The measured data will be used to assess and verify the self-weight consolidation properties with respect to the principles of soil mechanics.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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 designObservational
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

Citations3
Published2018
Admission routes2
Has abstractyes

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