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Examining the inconsistency of mercury flow in post-Minamata Convention global trade concerning artisanal and small-scale gold mining activity

2022· article· en· W4283521836 on OpenAlexaff
Yingchao Cheng, Kenichi Nakajima, Keisuke Nansai, Jacopo Seccatore, Marcello M. Veiga, Masaki Takaoka

Bibliographic record

VenueResources Conservation and Recycling · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMercury (programming language)ChinaConventionBusinessNatural resource economicsGeographyInternational tradeEconomicsPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

In 2017, the Minamata Convention (MC) on mercury (Hg) control entered into force. However, whether the MC is effective and how it reshapes the global Hg flow remain unclear. In this study, we established a method to detect inconsistencies in data on global Hg trade, and calculated the gap between the demand and supply of Hg to the artisanal and small-scale gold mining (ASGM) sector (i.e., the largest source of Hg emissions globally) in 39 countries across four regions. According to our results, inconsistencies in statistical data concerning Hg for ASGM activities exist in both Africa and Central and South America. Asia showed a considerably lower amount of Hg applied to ASGM than apparent Hg consumption; nevertheless, the largest consumer of Hg was Asia, predominantly China and India. Many countries in which ASGM is conducted are already MC parties; however, only few submitted their national action plans (NAPs) or have established/enforced specific laws to curb Hg use in ASGM. Analysis of Hg-related trade information suggests that in 2017, the trade of metallic Hg disappeared in some African and Central and South American countries, but new trade flows of goods with higher Hg content emerged. The method established in this study can support the search for countries implementing ASGM with hidden Hg use and flows, thereby contributing to the planning of further Hg control regulations. To enforce sound Hg management, the submission of NAPs should also be promoted in addition to the expansion of MC parties.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.040
GPT teacher head0.251
Teacher spread0.211 · 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 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

Citations16
Published2022
Admission routes1
Has abstractyes

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