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Record W4283590667 · doi:10.3390/min12070809

Sustainable Production of Rare Earth Elements from Mine Waste and Geoethics

2022· article· en· W4283590667 on OpenAlexafffund
Marouen Jouini, Alexandre Royer-Lavallée, Thomas Pabst, Eunhyea Chung, Rina Kim, Young‐Wook Cheong, Carmen Mihaela Neculita

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsPolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
FundersMinistry of Science and ICT, South KoreaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsKorea Institute of Geoscience and Mineral ResourcesMinistère des relations internationales et de la Francophonie
KeywordsResource depletionContext (archaeology)Environmental scienceSustainabilityEnvironmental protectionNatural resource economicsEcologyGeography

Abstract

fetched live from OpenAlex

The vulnerability of the rare earth element (REE) supply in a global context of increasing demands entails important economic and political issues, and has encouraged several countries to develop their own REE production projects. This study comparatively evaluated the production of REEs from primary and secondary resources in terms of their sustainability and contribution to the achievement of the Geoethics concept as responsibility towards oneself, colleagues, society, and the Earth system. Twelve categories of potential environmental and social impacts were selected: human health toxicity, global warming or climate change, terrestrial and aquatic eutrophication, acidification potential, particulate matter, resource depletion, water consumption, fresh water ecotoxicity, ionizing radiation, fossil fuel consumption, and ozone depletion. The results showed that the environmental impact of REE production from secondary sources is much lower relative to primary sources. A comparison of conventional and non-conventional REE resources showed that significant impact categories were related to particulate matter formation, abiotic resource depletion, and fossil fuel depletion, which could result from avoiding the tailings disposal before reuse. Based on these findings, governments and stakeholders should be encouraged to increase the recycling of secondary REE sources with Geoethics in mind, in order to balance the high demand of REEs while minimizing the overexploitation of non-renewable resources.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations37
Published2022
Admission routes2
Has abstractyes

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