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Recent advances in understanding and measurement of mercury in the environment: Terrestrial Hg cycling

2020· article· en· W3009053056 on OpenAlexaff
Kevin Bishop, James B. Shanley, Ami L. Riscassi, Heleen A. de Wit, Karin Eklöf, Bo Meng, Carl P. J. Mitchell, Stefan Osterwalder, Paul F. Schuster, Jackson P. Webster, Wei Zhu

Bibliographic record

VenueThe Science of The Total Environment · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMercury (programming language)Terrestrial ecosystemEnvironmental scienceCyclingEcosystemSurface runoffTerrestrial plantEnvironmental chemistryEarth scienceEcologyGeographyChemistryForestryGeology

Abstract

fetched live from OpenAlex

This review documents recent advances in terrestrial mercury cycling. Terrestrial mercury (Hg) research has matured in some areas, and is developing rapidly in others. We summarize the state of the science circa 2010 as a starting point, and then present the advances during the last decade in three areas: land use, sulfate deposition, and climate change. The advances are presented in the framework of three Hg “gateways” to the terrestrial environment: inputs from the atmosphere, uptake in food, and runoff with surface water. Among the most notable advances: The Arctic has emerged as a hotbed of Hg cycling, with high stream fluxes and large stores of Hg poised for release from permafrost with rapid high-latitude warming. The bi-directional exchange of Hg between the atmosphere and terrestrial surfaces is better understood, thanks largely to interpretation from Hg isotopes; the latest estimates place land surface Hg re-emission lower than previously thought. Artisanal gold mining is now thought responsible for over half the global stream flux of Hg. There is evidence that decreasing inputs of Hg to ecosystems may bring recovery sooner than expected, despite large ecosystem stores of legacy Hg. Freshly deposited Hg is more likely than stored Hg to methylate and be incorporated in rice. Topography and hydrological connectivity have emerged as master variables for explaining the disparate response of THg and MeHg to forest harvest and other land disturbance. These and other advances reported here are of value in evaluating the effectiveness of the Minamata Convention on reducing environmental Hg exposure to humans and wildlife.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.257
Teacher spread0.192 · 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 designNot applicable
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

Citations204
Published2020
Admission routes1
Has abstractyes

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