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Record W3110089738 · doi:10.1051/e3sconf/202019506006

Amelioration of permeable soil with green liquor dregs for the construction of sealing layers for mine waste storage facilities

2020· article· en· W3110089738 on OpenAlexaff
Ida Kronsell, Susanne Nigéus, Anna Virolainen, Yu Jia, Thomas Pabst, Christian Marurice

Bibliographic record

VenueE3S Web of Conferences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsPolytechnique Montréal
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsWaste managementBentoniteEnvironmental scienceHydraulic conductivityDrainageEnvironmental engineeringGeotechnical engineeringSoil waterGeologyEngineeringSoil science

Abstract

fetched live from OpenAlex

Mining of sulfidic ore generates acidic waste which often leads to the generation of acid rock drainage (ARD) having an adverse impact on aquatic flora and fauna. Engineered multilayer cover is one of the commonly used methods to prevent oxygen from being transported to mine waste. The sealing layer has a key function in the cover, thanks to its high water retention capacity and a low hydraulic conductivity, which enable the layer to remain near water saturation, effectively hindering oxygen diffusion. When adequate material is not available improvement of local soil material with a sealing agent is principally the only option to produce sealing layer material. Apart from bentonite clay, Green Liquor Dregs (GLD), a waste from the pulp and paper industry, have the ability to reduce the oxygen diffusion when mixed to granular soil, improving the water retention capacity of the blend. Experience from the development of Till/GLD-blends for the construction of sealing layers is compiled to address issue related to soil amelioration using waste with focus on material variation and quality control. The presentation focuses on the opportunities and challenges for the establishment of a circular system based on reutilization of a waste, herein GLD for improvement of soil’s geotechnical property.

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 score0.039
Threshold uncertainty score0.177

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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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