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Record W2134208072 · doi:10.1029/2004wr003219

Speciation and transport of newly deposited mercury in a boreal forest wetland: A stable mercury isotope approach

2005· article· en· W2134208072 on OpenAlexafffundabout
Brian A. Branfireun, David P. Krabbenhoft, Holger Hintelmann, Randall J. Hunt, James P. Hurley, John W. M. Rudd

Bibliographic record

VenueWater Resources Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsIsland HealthTrent UniversityUniversity of Toronto
FundersU.S. Geological SurveyUniversity of Alberta
KeywordsMercury (programming language)MethylmercuryEnvironmental chemistryWetlandPeatGroundwaterEnvironmental scienceWater tableBorealStable isotope ratioTaigaHydrology (agriculture)Aquatic ecosystemIsotopeChemistryEcologyGeologyBioaccumulation

Abstract

fetched live from OpenAlex

As part of the Mercury Experiment to Assess Atmospheric Loadings in Canada and the United States (METAALICUS) the fate and transport of contemporary mercury (Hg) deposition in a boreal wetland was investigated using an experimentally applied stable mercury isotope. We applied high purity (99.2% ± 0.1) 202 Hg(II) to a wetland plot to determine if (1) the 202 Hg was detectable above the pool of native Hg, (2) the 202 Hg migrated vertically and/or horizontally in peat and pore waters, and (3) the 202 Hg was converted to methylmercury (MeHg) in situ. The 202 Hg was easily detected by ICP/MS in both solid peat and pore waters. Over 3 months, the 202 Hg migrated vertically downward in excess of 15 cm below the water table and traveled several meters horizontally beyond the experimental plot to the lake margin along the dominant vector of groundwater flow. Importantly, at one location, 6% of aqueous 202 Hg was detected as Me 202 Hg after only 1 day. These results indicate that new inorganic Hg in atmospheric deposition can be readily methylated and transported lakeward by shallow groundwater flow, confirming the important role of wetlands as contributors of Hg to 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 score0.043
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.287
Teacher spread0.255 · 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

Citations81
Published2005
Admission routes3
Has abstractyes

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