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Record W7117126011 · doi:10.1038/s43247-025-03058-7

Carbon dynamics control contemporary mercury burial in Arctic Ocean sediments

2025· article· en· W7117126011 on OpenAlexafffundabout
Charles Gobeil, Sophia C. Johannessen, Miguel A. Goñi, Zou Zou A. Kuzyk, D. Cossa

Bibliographic record

VenueCommunications Earth & Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of ManitobaFisheries and Oceans CanadaInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMercury (programming language)Terrigenous sedimentArcticTotal organic carbonRemineralisationOrganic matterSedimentCanada Basin

Abstract

fetched live from OpenAlex

Understanding the high mercury concentrations observed in Arctic ecosystems requires in-depth knowledge of mercury cycling. Here, we show that variations in mercury concentration in the sediments of the North American Arctic Margin and the Arctic Ocean basins can be explained by carbon sources and cycling. Sedimentary mercury concentrations are predicted (p < 0.001) considering three carbon sources and, in some areas, the recapture of soluble mercury released during burial. Terrigenous organic carbon dominates mercury delivery (50-90%) in the western North American Arctic Margin, whereas inorganic carbon predominates at many eastern sites (40-70%). Marine organic carbon contributes <15% of the total mercury concentration. Mercury–organic carbon endmember ratios of terrigenous and marine organic carbon are higher in deep Arctic Ocean basins than in the continental margin, likely due to greater organic matter remineralization during transit. The observed enrichment in mercury toward the sediment surface results from remineralization of organic carbon, not from an increase in mercury flux. Mercury concentrations in sediments of the North American Arctic Margin and the Arctic Ocean basins can be entirely explained by carbon sources and cycling, according to analysis of sediment cores from the polar ocean.

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)0.000
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 score0.155
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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