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Record W6959210818 · doi:10.1021/acs.est.4c12263.s001

DiminishingMercuryBioaccumulation in Zooplanktonalong an Estuarine Salinity Gradient

2025· article· en· W6959210818 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsBioaccumulationMethylmercuryEstuaryTrophic levelMercury (programming language)ZooplanktonOrganic matterFood webSalinity

Abstract

fetched live from OpenAlex

Estuarine transition zones (ETZs) are biogeochemically complex, nutrient-rich environments supporting diverse and productive food webs. They may also be sites of microbial production of methylmercury (MeHg) and bioaccumulation of this neurotoxicant at the base of the food web. However, the environmental drivers controlling these mechanisms are unclear. We studied the pattern of MeHg bioaccumulation in zooplankton along the 200 km ETZ of a large North American estuary, the St. Lawrence Estuary (Québec, Canada). Our approach integrated the dynamic variation in ETZ geochemistry, focusing on MeHg speciation change, alongside ecological factors, including community composition and stable isotopic tracers of diet and habitat. MeHg bioaccumulation decreased with distance downstream along the ETZ, driven by the salinity gradient and traced by the isotopic signature of sulfur in zooplankton. MeHg speciation modeling suggested that complexation to dissolved organic matter may be used as a proxy of the trophic transfer of MeHg to zooplankton. Further, the binding of MeHg to organic matter was reduced by the presence of chloride, thus reducing the trophic transfer of MeHg. We propose a conceptual model for MeHg cycling in ETZ of large estuaries that hypothesizes that higher-level consumers in turbid upstream regions may face heightened risks of MeHg toxicity but that Hg levels diminish drastically as salinity increases.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.344
Teacher spread0.286 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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