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Record W2162774718 · doi:10.1897/02-644

Methylmercury production in high arctic wetlands

2004· article· en· W2162774718 on OpenAlexaff
Lisa L. Loseto, Steven D. Siciliano, D. R. S. Lean

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of SaskatchewanUniversity of Ottawa
Fundersnot available
KeywordsMethylmercuryWetlandArcticEnvironmental scienceProduction (economics)Environmental chemistryEcologyBiologyChemistryBioaccumulation

Abstract

fetched live from OpenAlex

Mercury is present at elevated levels in the top predators living in High Arctic ecosystems. Because only methylmercury (MeHg) bioaccumulates in food chains, the sources need to be identified. In temperate environments, wetlands are considered to be the principal sources of MeHg, with sulfate-reducing bacteria (SRB) thought to be responsible. The present study investigated whether High Arctic wetlands produced MeHg and whether SRB were involved in MeHg formation. Frozen soil was collected from 18 High Arctic wetlands before ground thaw, and when analyzed for MeHg, values were low, averaging 0.065 ng/g. When soils were incubated for 30 and 60 d at typical summer Arctic soil temperatures (4 degrees C and 8 degrees C), MeHg increased up to 100-fold. These laboratory observations were consistent with field measurements of wetland surface water, where MeHg concentrations increased from near detection limits (0.02 ng/L) at the inflow to an average of 1.21 ng/L at the outflow. Both laboratory and field data showed MeHg production in High Arctic wetlands. The prevalence of SRB in soil was low, however, and DNA analysis of the dissimilatory sulfate-reductase gene specific to SRB was positive at only one site. The present study showed that wetlands in the High Arctic can produce MeHg but that SRB may not the dominant mercury methylators.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.222
Teacher spread0.214 · 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.

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

Citations89
Published2004
Admission routes1
Has abstractyes

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