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Record W2030511510 · doi:10.1038/npre.2007.690.1

Chlorinated Diphenyl Ethers in Sediments, Biota, and the Water Column from Coastal British Columbia, Canada

2007· preprint· en· W2030511510 on OpenAlexaffabout
Sierra Rayne, Ikonomou Michael, Chris Garrett

Bibliographic record

VenueNature Precedings · 2007
Typepreprint
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaFisheries and Oceans CanadaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental chemistryCongenerBiotaBiomagnificationPolybrominated diphenyl ethersChemistryEnvironmental scienceContaminationFood chainPollutantBioaccumulationEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Polychlorinated diphenyl ethers (PCDEs) are impurities in technical chlorophenol mixtures, are used as dielectric fluids in capacitors and as herbicides, antiseptics, food preservatives, and papers and textiles, and can be formed through the chlorination of organics in water and wastewater streams. Samples from semipermeable membrane devices (SPMDs), sediments, English sole, mussels, and Dungeness crab were collected from urban/industrial and remote sites along the marine coast of British Columbia and analyzed by congener-specific GC-MS analysis for mono- through deca-substituted PCDEs. Higher concentrations in biota and sediments were observed near urban/industrial areas, with evidence for food-chain biomagnification within major harbours. Correlations between size distributions and PCDE levels indicate these hydrophobic contaminants tend to preferentially partition into sediments with higher clay/silt fractions. Multivariate analysis of congener patterns shows distinct PCDE profiles among different species and environmental matrices, and evidence for favoured dechlorination pathways among the most commonly observed congeners in aquatic systems.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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