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Arctic methylmercury cycling

2022· article· en· W4286687397 on OpenAlexafffund
Sofi Jonsson, Michelle Nerentorp Mastromonaco, Fei Wang, Andrea G. Bravo, Warren R. L. Cairns, John Chételat, Thomas A. Douglas, Gretchen L. Lescord, Liisa Ukonmaanaho, Lars‐Éric Heimbürger‐Boavida

Bibliographic record

VenueThe Science of The Total Environment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsWildlife Conservation Society CanadaLaurentian UniversityEnvironment and Climate Change CanadaUniversity of Manitoba
FundersVetenskapsrådetAXA Research FundEnvironment and Climate Change CanadaSvenska Forskningsrådet Formas
KeywordsMethylmercuryArcticEnvironmental chemistryBioaccumulationBiogeochemical cycleEnvironmental scienceBiomagnificationThermokarstPermafrostBiotaArctic vegetationEcologyEcosystemBiogeochemistryMercury (programming language)Arctic ecologyOceanographyTundraChemistryGeologyBiology

Abstract

fetched live from OpenAlex

Anthropogenic mercury (Hg) undergoes long-range transport to the Arctic where some of it is transformed into methylmercury (MeHg), potentially leading to high exposure in some Arctic inhabitants and wildlife. The environmental exposure of Hg is determined not just by the amount of Hg entering the Arctic, but also by biogeochemical and ecological processes occurring in the Arctic. These processes affect MeHg uptake in biota by regulating the bioavailability, methylation and demethylation, bioaccumulation and biomagnification of MeHg in Arctic ecosystems. Here, we present a new budget for pools and fluxes of MeHg in the Arctic and review the scientific advances made in the last decade on processes leading to environmental exposure to Hg. Methylation and demethylation are key processes controlling the pool of MeHg available for bioaccumulation. Methylation of Hg occurs in diverse Arctic environments including permafrost, sediments and the ocean water column, and is primarily a process carried out by microorganisms. While microorganisms carrying the hgcAB gene pair (responsible for Hg methylation) have been identified in Arctic soils and thawing permafrost, the formation pathway of MeHg in oxic marine waters remains less clear. Hotspots for methylation of Hg in terrestrial environments include thermokarst wetlands, ponds and lakes. The shallow sub-surface enrichment of MeHg in the Arctic Ocean, in comparison to other marine systems, is a possible explanation for high MeHg concentrations in some Arctic biota. Bioconcentration of aqueous MeHg in bacteria and algae is a critical step in the transfer of Hg to top predators, which may be dampened or enhanced by the presence of organic matter. Variable trophic position has an important influence on MeHg concentrations among populations of top predator species such as ringed seal and polar bears distributed across the circumpolar Arctic. These scientific advances highlight key processes that affect the fate of anthropogenic Hg deposited to Arctic environments.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.017
GPT teacher head0.239
Teacher spread0.223 · 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 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

Citations40
Published2022
Admission routes2
Has abstractyes

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