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Record W2551326609 · doi:10.1139/cgj-2016-0375

Performance of polymer-enhanced bentonite–sand mixture for covering arsenic-rich gold mine tailings for up to 4 years

2016· article· en· W2551326609 on OpenAlexafffundvenue
Mohamed Hosney, R. Kerry Rowe

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Resources Canada
KeywordsTailingsSubgradeBentoniteArsenicGeosynthetic clay linerHydraulic conductivityGeotechnical engineeringGeologySoil waterMaterials scienceMetallurgySoil science

Abstract

fetched live from OpenAlex

Experiments conducted over a 4 year period, on a polymer-enhanced bentonite–sand mixture (PEBSM) used as cover for gold mine tailings are reported. The effect on PEBSM hydraulic conductivity (k) of subgrade porewater chemistry, subgrade water content, and confining stress are investigated. Results show that the reduction in the mole fraction of bound Na + (ESP) and corresponding increase in k of PEBSM with time was highly dependent on the ionic strength of the subgrade porewater. When the PEBSM was in direct contact with gold mine tailings with porewater having an ionic strength of 145 mmol/L, ESP decreased from 59% to 2% and k increased from 4 × 10 −11 to 6.9 × 10 −9 m/s. The ESP and k values of PEBSM over tailings with 11 mmol/L porewater were 21% and 6.9 × 10 −11 m/s, respectively. A 0.15 m thick foundation layer between tailings and PEBSM layer significantly lowered the reduction in ESP and increase in k with time as did a reduction in the subgrade water content. There was no effect of changing confining stress from 15 to 7.5 kPa on k values of PEBSM. The PEBSM layer acted as a good barrier to the migration of arsenic from tailings upward towards cover soil above the PEBSM layer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.517

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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 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

Citations18
Published2016
Admission routes3
Has abstractyes

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