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Record W2892166336 · doi:10.1038/s41598-018-32760-0

Subsurface seawater methylmercury maximum explains biotic mercury concentrations in the Canadian Arctic

2018· article· en· W2892166336 on OpenAlexafffundabout
Kang Wang, Kathleen M. Munson, Alexis Beaupré-Laperrière, Alfonso Mucci, Robie W. Macdonald, Fei Wang

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsFisheries and Oceans CanadaMcGill UniversityUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsArcticNet
KeywordsBiotaMethylmercuryArcticOceanographyMercury (programming language)Environmental scienceBiomagnificationSeawaterEcosystemTrophic levelMarine ecosystemZooplanktonEcologyEnvironmental chemistryGeologyBioaccumulationChemistryBiology

Abstract

fetched live from OpenAlex

Mercury (Hg) is a contaminant of major concern in Arctic marine ecosystems. Decades of Hg observations in marine biota from across the Canadian Arctic show generally higher concentrations in the west than in the east. Various hypotheses have attributed this longitudinal biotic Hg gradient to regional differences in atmospheric or terrestrial inputs of inorganic Hg, but it is methylmercury (MeHg) that accumulates and biomagnifies in marine biota. Here, we present high-resolution vertical profiles of total Hg and MeHg in seawater along a transect from the Canada Basin, across the Canadian Arctic Archipelago (CAA) and Baffin Bay, and into the Labrador Sea. Total Hg concentrations are lower in the western Arctic, opposing the biotic Hg distributions. In contrast, MeHg exhibits a distinctive subsurface maximum at shallow depths of 100-300 m, with its peak concentration decreasing eastwards. As this subsurface MeHg maximum lies within the habitat of zooplankton and other lower trophic-level biota, biological uptake of subsurface MeHg and subsequent biomagnification readily explains the biotic Hg concentration gradient. Understanding the risk of MeHg to the Arctic marine ecosystem and Indigenous Peoples will thus require an elucidation of the processes that generate and maintain this subsurface MeHg maximum.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.267
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations73
Published2018
Admission routes3
Has abstractyes

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