MétaCan
Menu
Back to cohort
Record W2021263707 · doi:10.1021/es061980b

Biological and Chemical Redox Transformations of Mercury in Fresh and Salt Waters of the High Arctic during Spring and Summer

2007· article· en· W2021263707 on OpenAlexafffund
Alexandre J. Poulain, Édenise Garcia, Marc Amyot, Peter G. C. Campbell, Farhad Raofie, Parisa A. Ariya

Bibliographic record

VenueEnvironmental Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité de MontréalMcGill UniversityInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersCanadian Foundation for Climate and Atmospheric SciencesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMercury (programming language)Spring (device)Environmental chemistryArcticThe arcticEnvironmental scienceRedoxChemistryOceanographyGeologyInorganic chemistryEngineering

Abstract

fetched live from OpenAlex

It is well-established that atmospheric deposition transports Hg to Arctic regions, but the postdepositional dynamics of Hg that can alter its impact on Arctic food chains are less understood. Through a series of in situ experiments, we investigated the redox transformations of Hg in coastal and inland aquatic systems. During spring and summer, Hg reduction in streams and pond waters decreased across a 4-fold increase in salinity. This alteration of Hg reduction due to chloride was counterbalanced by the presence of particles, which favored the conversion of oxidized Hg to its elemental form. In saline waters, biogenic organic materials, produced by algae, were able to promote oxidation of Hg(O) even under dark conditions. Overall these results point to the vulnerability of marine/ coastal Arctic systems to Hg, compared to inland systems, with oxidation processes enhancing Hg residence times and thus increasing its potential to enter the food chain.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
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 studies0.0000.005
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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations62
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicMercury impact and mitigation studiesFrench-language works237,207