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Record W4393981730 · doi:10.36487/acg_repo/2455_42

Development of slag alternatives for paste backfill operations

2024· article· en· W4393981730 on OpenAlexafffundabout
NA Romaniuk, L McFarlane, N Hariharan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsGraymont (Canada)
FundersGraymont
KeywordsSlag (welding)Waste managementComputer scienceEngineeringMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Paste backfilling, a critical step of the underground mining cycle which enables both increased resource recovery and provides a resilient tailings storage solution, is increasingly challenged by limited availability of industrial byproduct binders such as ground granulated blast furnace slag (GGBFS). The use of high quality GGBFS has proven to be critical for operators to address challenges posed by unique and complex ore compositions such as high sulphate ores while also contributing towards reducing the scope 3 greenhouse gas (GHG) emissions, especially as traditional cement binders can be responsible for up to 70% of the GHG emissions in the backfill process. This paper focuses on the development of a versatile engineered lime-based binder for paste backfill which maintains a low GHG footprint and can be made adaptable to various mine conditions, such as high sulphate ores, without compromising strength and other performance requirements. The novel binder has been tested in laboratory conditions by monitoring the unconfined compressive strength development over time using a sulphate-rich paste tailings provided by a commercial mining operation in Canada and has shown promising progress as a slag alternative.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score0.139

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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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 designOther design
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

Citations2
Published2024
Admission routes3
Has abstractyes

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