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Record W4409165734 · doi:10.3390/min15040376

Four Decades of Efforts to Reduce or Eliminate Mercury Pollution in Artisanal Gold Mining

2025· article· en· W4409165734 on OpenAlexafffund
Marcello M. Veiga, Nnamdi C. Anene, Emiliano M. Silva

Bibliographic record

VenueMinerals · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMercury pollutionGold miningMercury (programming language)PollutionEnvironmental scienceEnvironmental protectionMining engineeringEnvironmental planningNatural resource economicsGeologyChemistryComputer scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Throughout the past four decades, most projects related to mercury in Artisanal Gold Mining (AGM) have been dedicated to monitoring the environmental and health impacts of the activity without actually proposing effective solutions to tackle the issue. Recently, the UN and a few NGOs have been dedicated to bringing solutions to artisanal gold miners, but the outcomes remain modest, given the funds expended and the considerable effort invested by interventionists. This commentary paper critiques some of the interventions observed in the last four decades and suggests some technical strategies to approach artisanal miners to reduce mercury losses. It is stressed that mercury elimination is a consequence of good engagement with miners that creates opportunities to show them how to produce more gold with cleaner methods. We recommend that academics educate a new generation of engineers working with AGM to adopt a more practical approach, ensuring they understand the needs, motivations, and skills of artisanal miners before proposing solutions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.314
Teacher spread0.281 · 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

Citations10
Published2025
Admission routes2
Has abstractyes

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