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Record W2082114100 · doi:10.1021/es061314a

Biologically Mediated Transport of Contaminants to Aquatic Systems

2007· article· en· W2082114100 on OpenAlexafffund
Jules M. Blais, Robie W. Macdonald, Donald Mackay, Eva Webster, Colin J. B. Harvey, John P. Smol

Bibliographic record

VenueEnvironmental Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsTrent UniversityQueen's UniversityUniversity of OttawaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioaccumulationEnvironmental scienceContaminationFish <Actinopterygii>Aquatic ecosystemEcologyBiologyFishery

Abstract

fetched live from OpenAlex

The prevailing view is that long-range transport of semivolatile contaminants is primarily conducted by the physical system (e.g., winds, currents), and biological transport is typically ignored. Although this view may be correct in terms of bulk budgets and fluxes, it neglects the potential of animals to focus contaminants into foodwebs due to their behaviors and lifecycles. In particular, gregarious animals that biomagnify and bioaccumulate certain contaminants and then migrate and congregate can become the predominant pathway for contaminants in many circumstances. Fish and birds provide prominent examples for such behavior. This review examines the potential for biovector transport to expose populations to contaminants. In addition, we apply a modeling approach to compare the potential of biovector transport to other physical transport pathways for a hypothetical lake receiving large numbers of fish. We conclude that biovector transport should not be neglected when considering environmental risks of biomagnifying contaminants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations285
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicToxic Organic Pollutants ImpactFrench-language works237,207