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Record W2773527722 · doi:10.1021/acs.est.7b03821

A Model for Methylmercury Uptake and Trophic Transfer by Marine Plankton

2017· article· en· W2773527722 on OpenAlexaff
Amina T. Schartup, Asif Qureshi, Clifton Dassuncao, Colin P. Thackray, Gareth Harding, Elsie M. Sunderland

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsBedford Institute of Oceanography
FundersDivision of Ocean SciencesU.S. Environmental Protection Agency
KeywordsPhytoplanktonTrophic levelZooplanktonMethylmercuryPlanktonFood webEcosystemEnvironmental scienceMarine ecosystemBioaccumulationEnvironmental chemistryOceanographyChemistryEcologyNutrientBiologyGeology

Abstract

fetched live from OpenAlex

Methylmercury (MeHg) concentrations can increase by 100 000 times between seawater and marine phytoplankton, but levels vary across sites. To better understand how ecosystem properties affect variability in planktonic MeHg concentrations, we develop a model for MeHg uptake and trophic transfer at the base of marine food webs. The model successfully reproduces measured concentrations in phytoplankton and zooplankton across diverse sites from the Northwest Atlantic Ocean. Highest MeHg concentrations in phytoplankton are simulated under low dissolved organic carbon (DOC) concentrations and ultraoligotrophic conditions typical of open ocean regions. This occurs because large organic complexes bound to MeHg inhibit cellular uptake and cell surface area to volume ratios are greatest under low productivity conditions. Modeled bioaccumulation factors for phytoplankton (10 2.4 –10 5.9 ) are more variable than those for zooplankton (10 4.6 –10 6.2 ) across ranges in DOC (40–500 μM) and productivities (ultraoligotrophic to hypereutrophic) typically found in marine ecosystems. Zooplankton growth dilutes their MeHg body burden, but they also consume greater quantities of MeHg enriched prey at larger sizes. These competing processes lead to lower variability in MeHg concentrations in zooplankton compared to phytoplankton. Even under hypereutrophic conditions, modeled growth dilution in marine zooplankton is insufficient to lower their MeHg concentrations, contrasting findings from freshwater 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

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 studies0.0010.005
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations127
Published2017
Admission routes1
Has abstractyes

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