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Record W2547501295 · doi:10.1139/er-2016-0042

Initiatives to combat mercury use in artisanal small-scale gold mining: A review on issues and challenges

2016· review· en· W2547501295 on OpenAlexvenueno aff
Tshia Malehase, Adegbenro P. Daso, Jonathan O. Okonkwo

Bibliographic record

VenueEnvironmental Reviews · 2016
Typereview
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersDeutscher Akademischer AustauschdienstNational Research Foundation
KeywordsGold miningMercury (programming language)CompendiumPovertyEnvironmental planningBusinessEnvironmental resource managementPolitical scienceEnvironmental scienceGeographyComputer scienceLaw

Abstract

fetched live from OpenAlex

The Minamata Convention on mercury has received a number of criticisms and challenges that potentially hinder its progress on reducing and controlling mercury use and release by artisanal small-scale gold mining (ASSGM). The resulting weak environmental control has repercussions for the social and environmental wellbeing of countries that subsist on ASSGM in their territory. Lack of distinguishing and categorizing ASSGM, the absence of a contextual implementation plan, and no defined means of communication are some of the aspects that lead to unsuccessful initiatives, particularly on effectively introducing mercury-free technologies. Moreover, an underestimation of the active mining population implies that the problem is greater than what is perceived. ASSGM is a viable source of poverty alleviation which cannot be ignored and therefore the resulting socioeconomic and environmental challenges need to be addressed while optimizing economic benefits. In this paper a compendium of issues and challenges that need to be addressed to reduce and control mercury use and release by ASSGM are discussed. By reviewing the challenges of successful case studies, a comprehensive approach is proposed to enhance the implementation of the Minamata Convention on mercury.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.001

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 head0.095
GPT teacher head0.290
Teacher spread0.195 · 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 designOther design
Domainnot available
GenreReview

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

Citations21
Published2016
Admission routes1
Has abstractyes

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