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Record W2060295533 · doi:10.1897/07-651.1

Partitioning of current-use and legacy pesticides in salmon habitat in British Columbia, Canada

2008· article· en· W2060295533 on OpenAlexaffabout
Kate Harris, Neil Dangerfield, Million B. Woudneh, Stacey Verrin, Peter S. Ross

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsAXYS Technologies (Canada)Fisheries and Oceans Canada
Fundersnot available
KeywordsPesticideHabitatCurrent (fluid)Environmental scienceFisheryEcologyGeographyEnvironmental protectionBiologyOceanographyGeology

Abstract

fetched live from OpenAlex

Current regulatory paradigms have favored a shift from persistent pesticides that amplify in aquatic food webs to pesticides with reduced persistence and bioaccumulative potential (low log K(OW)). Although these new generation pesticides preferentially partition away from food web-associated lipids, aquatic biota may nonetheless be exposed to them via other environmental compartments. To characterize pesticide patterns in coho salmon (Oncorhynchus kisutch) habitat, we studied two salmon-bearing watersheds (agricultural and urban) in British Columbia, Canada's Fraser River valley and one in a remote area of the province's central coast. The agricultural and remote sites exhibited pesticide patterns dominated by current-use pesticides, whereas the urban site was largely dominated by legacy organochlorine pesticides. When adjusted to trans-chlordane concentrations across environmental matrices, correlations were observed between water to sediment ratios for the pesticides and their octanol:water partitioning coefficients (log K(OW); r2=0.48, p < 0.0001); between air to water ratios and Henry's Law coefficients (r2=0.55, p<0.0001); and between fish to water ratios and log K(OW) (r2=0.74, p<0.0001). These relationships underscore the importance of physicochemical properties in determining the fate of pesticides in freshwater salmon habitat, and highlight the need for research on the nature of health risks associated with exposure where little or no accumulation occurs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations21
Published2008
Admission routes2
Has abstractyes

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