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Record W4292994701 · doi:10.5382/econgeo.4937

Antimony in Mine Wastes: Geochemistry, Mineralogy, and Microbiology

2022· article· en· W4292994701 on OpenAlexaff
Anežka Borčinová Radková, Heather E. Jamieson, Kate M. Campbell, Karen A. Hudson‐Edwards

Bibliographic record

VenueEconomic Geology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsQueen's University
Fundersnot available
KeywordsAntimonyStibniteEnvironmental chemistryBiogeochemical cycleTailingsArsenicEnvironmental remediationGeochemistryChemistryMetalloidContext (archaeology)Environmental scienceGeologyContaminationMetalPyriteInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Antimony (Sb) is a valuable mined commodity, used mostly in fire retardants, and considered a critical element. It is also a potential environment hazard classed as a carcinogen. Antimony is concentrated in tailings and waste rock from Sb mines as well as other locations, such as precious metal deposits, where Sb is present in the ore but not recovered. This review covers the aqueous geochemistry, isotope chemistry, mineralogy, and microbiology of Sb in the context of mine waste. The primary minerals stibnite and sulfosalts may release Sb in surface and groundwaters and result in contamination of soils, plants, and river sediments. In some cases, Sb mobility is limited by its adsorption and incorporation into Fe (oxyhydr)oxides. At higher Sb concentrations, precipitation of Sb secondary hosts such as tripuhyite (FeSbO4, relatively insoluble) and brandholzite (Mg[Sb(OH)6]2 · 6H2O, highly soluble) influence Sb concentrations in water associated with mine waste. Although Sb is nonessential to organisms, microorganisms are involved in oxidation, reduction, and methylation processes that can drive biogeochemical transformations. Limited toxicological information about Sb makes it challenging to establish regulations or guidelines limiting the concentration of Sb. Antimony is frequently associated with arsenic in mine waste, and remediation design is often based on the assumption that both metalloids behave in a similar way. However, new research suggests that in some environments, this is not the case, and Sb should be considered based on its unique biogeochemical behavior.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.988

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)0.0130.000

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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.

Study designObservational
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

Citations25
Published2022
Admission routes1
Has abstractyes

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