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Record W4385492175 · doi:10.21741/9781644902592-21

Recycled smelter slag as an engineering material - opportunity and sustainability

2023· article· en· W4385492175 on OpenAlexaboutno aff
S. Roy Chowdhury

Bibliographic record

VenueMaterials research proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsnot available
Fundersnot available
KeywordsSmeltingWaste managementSlag (welding)LimeResource recoveryEnvironmental scienceIndustrial wasteEngineeringMaterials scienceMetallurgyWastewater

Abstract

fetched live from OpenAlex

Abstract. Slags obtained from the Vale Copper Cliff smelter in Sudbury, Ontario, Canada, were investigated as sustainable engineering materials in this study. The recycled smelter waste products can remove toxic contaminants from the aqueous environment as well as be used in the construction industry (as aggregates, cement admixtures, filling materials), soil improvement for agricultural purposes, and other value-added applications and products. The removal mechanisms of the heavy metals (such as Zn, Pb, and Cu, etc.) from aqueous solutions could be physical or chemical adsorption, ion exchange, oxidation-reduction, etc. At the same time, using recycled smelter slags in various engineering applications can help with waste reduction, disposal cost reduction, resource recovery, and increased reused activities. The present study helps explore the scope of using recycled materials in the treatment or construction industry. Using industrial smelter slag as a recycling or renewable resource rather than a waste product has environmental and economic benefits. The study also specifically discusses Ni smelter slag's composition, application, treatment efficiency, opportunity, economic benefits, and circularity for sustainable management.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.059
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.081
GPT teacher head0.357
Teacher spread0.276 · 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
Published2023
Admission routes1
Has abstractyes

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