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Record W2019149999 · doi:10.1021/es901193x

Beaver Ponds Increase Methylmercury Concentrations in Canadian Shield Streams along Vegetation and Pond-Age Gradients

2009· article· en· W2019149999 on OpenAlexafffundabout
Virginie Roy, Marc Amyot, Richard Carignan

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethylmercuryBeaverEnvironmental chemistryNutrientDissolved organic carbonOrganic matterEnvironmental scienceMercury (programming language)Hydrology (agriculture)ChemistryNitrateEcologyBioaccumulationGeology

Abstract

fetched live from OpenAlex

Beaver impoundments flood forested areas and may be important production sites for methylmercury (MeHg) because of the resulting enhanced microbial activity and oxygen depletion. The influence of 17 beaver impoundments on streamwater chemistry (total mercury (THg), MeHg, nutrients, cations, and anions)] was investigated by sampling sites located along vegetation and pond-age gradients in southwestern Quebec (Canada). Recently inundated beaver ponds (< 10 years old) and those located in coniferous watersheds had the highest MeHg concentrations (range, 0.10-4.53 ng L(-1)) and greatest methylation efficiencies (% THg as MeHg; range, 10-74%). High heterotrophic activity likely occurred in the beaver ponds as suggested by depletions of dissolved oxygen, sulfate and nitrite-nitrate concentrations, and increases in nutrients (e.g., dissolved organic carbon, total phosphorus, and total nitrogen) in outlets compared to inlets. Acidic waters at coniferous sites may have stimulated more MeHg production than in mixed woodland regions. Lower methylation efficiencies in older ponds (> 20 years old) may be due to the degradation of less labile organic matter as ponds age. Beavers actively alter watersheds by building impoundments, and our findings indicate that this landscape disturbance may be a significant source of MeHg to downstream water bodies.

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 score0.142
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations73
Published2009
Admission routes3
Has abstractyes

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