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Record W2021682321 · doi:10.1021/es0508828

Mercury Methylation in the Epilithon of Boreal Shield Aquatic Ecosystems

2006· article· en· W2021682321 on OpenAlexaff
Mélanie Desrosiers, Dolors Planas, Alfonso Mucci

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMethylmercuryPeriphytonAquatic ecosystemEnvironmental chemistryMercury (programming language)PhotosynthesisEcologyBiologyMethylationAlgaeChemistryBotanyBioaccumulation

Abstract

fetched live from OpenAlex

Methylation rates by periphyton growing on the rocky shore of a remote boreal shield lake were measured over diurnal cycles at temperatures representative of summer and fall conditions. The measurements were carried out in vitro with natural communities grown on artificial Teflon substrates submerged along the lake's shore for 1-2 years. At temperatures above 20 degrees C, epilithon Hg methylation rates were fast and reached a steady state within 12 h upon exposure to 2 ng L(-1) of inorganic mercury. A variety of inhibitors were used to identify which microorganisms in the epilithic biofilm are responsible for the methylation. The addition of molybdate, which is believed to suppress the activity of sulfate-reducing bacteria, decreased methylmercury production rates by 60% in both light and dark experiments. The prokaryote inhibitor chloramphenicol reduced the methylation rate by 40% only during dark periods whereas an algal inhibitor (DCMU), which suppresses photosynthesis, decreased the methylation rate by 60% during light periods. Results of this study reveal that epilithon communities may be a significant source of MeHg to higher aquatic organisms in lakes and that the integrity of the epilithic biofilm is important for its ability to methylate Hg.

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 score0.124
Threshold uncertainty score0.774

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 communication0.0000.000
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 head0.009
GPT teacher head0.238
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

Citations93
Published2006
Admission routes1
Has abstractyes

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