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Record W647743169 · doi:10.1021/acs.est.5b00763

High Methylmercury in Arctic and Subarctic Ponds is Related to Nutrient Levels in the Warming Eastern Canadian Arctic

2015· article· en· W647743169 on OpenAlexafffundabout
Gwyneth A. MacMillan, Catherine Girard, John Chételat, Isabelle Laurion, Marc Amyot

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsInstitut National de la Recherche ScientifiqueEnvironment and Climate Change CanadaUniversité de MontréalCenter for Northern Studies
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaAboriginal Affairs and Northern Development CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsSubarctic climateArcticMethylmercuryEnvironmental scienceNutrientThe arcticArctic ecologyOceanographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Permafrost thaw ponds are ubiquitous in the eastern Canadian Arctic, yet little information exists on their potential as sources of methylmercury (MeHg) to freshwaters. They are microbially active and conducive to methylation of inorganic mercury, and are also affected by Arctic warming. This multiyear study investigated thaw ponds in a discontinuous permafrost region in the Subarctic taiga (Kuujjuarapik-Whapmagoostui, QC) and a continuous permafrost region in the Arctic tundra (Bylot Island, NU). MeHg concentrations in thaw ponds were well above levels measured in most freshwater ecosystems in the Canadian Arctic (>0.1 ng L(-1)). On Bylot, ice-wedge trough ponds showed significantly higher MeHg (0.3-2.2 ng L(-1)) than polygonal ponds (0.1-0.3 ng L(-1)) or lakes (<0.1 ng L(-1)). High MeHg was measured in the bottom waters of Subarctic thaw ponds near Kuujjuarapik (0.1-3.1 ng L(-1)). High water MeHg concentrations in thaw ponds were strongly correlated with variables associated with high inputs of organic matter (DOC, a320, Fe), nutrients (TP, TN), and microbial activity (dissolved CO2 and CH4). Thawing permafrost due to Arctic warming will continue to release nutrients and organic carbon into these systems and increase ponding in some regions, likely stimulating higher water concentrations of MeHg. Greater hydrological connectivity from permafrost thawing may potentially increase transport of MeHg from thaw ponds to neighboring aquatic ecosystems.

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 score0.013
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations80
Published2015
Admission routes3
Has abstractyes

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