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Record W2050932513 · doi:10.1039/b606960f

Sediment influence on congener-specific PCB bioaccumulation by Mytilus edulis: a case study from an intertidal hot spot, Clyde Estuary, UK

2006· article· en· W2050932513 on OpenAlexfundno aff
P.J. Edgar, Andrew Hursthouse, J. E. Matthews, I. M. Davies, Stephen Hillier

Bibliographic record

VenueJournal of Environmental Monitoring · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsBioaccumulationSedimentEstuaryEnvironmental chemistryCongenerMytilusIntertidal zoneContaminationEnvironmental scienceBiotaGeologyChemistryOceanographyEcologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

An intertidal site in the Clyde Estuary, UK, was selected to evaluate the role of sediment geochemistry on the bioaccumulation of polychlorinated biphenyls (PCBs) by mussels (Mytilus edulis). The area had previously been identified as showing anomalously high levels of PCB contamination (over 1,500 microg kg(-1) total PCB in sediment, 22 congeners). Samples of surface sediment and M. edulis were collected from two closely located sites, one within the anomalous area and another representing typical PCB contamination in the estuary. Sediment samples were separated into grain size fractions and analysed for a range of biomarker compounds, PCBs and sediment mineralogy. The anomalous site showed an atypical association of PCBs with sediment properties, despite both locations showing influence of both petrogenic and pyrogenic organic contamination. Interrogation of data using correlation and principal component analysis showed that sediment mineralogy as well as organic matter composition influenced PCB congener distribution. One sediment source was found to control the PCB concentration in mussels at both locations and clay mineralogy appears to control PCB uptake by biota with preference for higher molecular weight congeners. Overall bioavailability is determined by sediment TOC.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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

Citations24
Published2006
Admission routes1
Has abstractyes

Explore more

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