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Record W2329639364 · doi:10.1021/es300577e

Methylmercury Cycling in High Arctic Wetland Ponds: Controls on Sedimentary Production

2012· article· en· W2329639364 on OpenAlexaffabout
Igor Lehnherr, Vincent L. St. Louis, Jane L. Kirk

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMethylmercuryBiomagnificationBioaccumulationEnvironmental chemistrySedimentArcticMercury (programming language)WetlandEnvironmental scienceEcologyChemistryGeologyBiology

Abstract

fetched live from OpenAlex

Methylmercury (MeHg) is a potent neurotoxin that has been demonstrated to biomagnify in Arctic freshwater foodwebs to levels that may be of concern to Inuit peoples subsisting on freshwater fish, for example. The key process initiating the bioaccumulation and biomagnification of MeHg in foodwebs is the methylation of inorganic Hg(II) to form MeHg, and ultimately how much MeHg enters foodwebs is controlled by the production and availability of MeHg in a particular water body. We used isotopically enriched Hg stable isotope tracers in sediment core incubations to measure potential rates of Hg(II) methylation and investigate the controls on MeHg production in High Arctic wetland ponds in the Lake Hazen region of northern Ellesmere Island (Nunavut, Canada). We show here that MeHg concentrations in sediments are primarily controlled by the sediment methylation potential and the quantity of Hg(II) available for methylation, but not by sediment demethylation potential. Furthermore, MeHg concentrations in pond waters are controlled by MeHg production in sediments, overall anaerobic microbial activity, and photodemethylation in the water column.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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

Citations52
Published2012
Admission routes2
Has abstractyes

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