Spatial trends of polybrominated diphenyl ethers in Canadian fish and implications for long-term monitoring
Bibliographic record
Abstract
A nationwide study was conducted to examine concentrations of polybrominated diphenyl ethers (PBDEs) in top predatory fish, with a focus on lake trout (Salvelinus namaycush), across Canada, and to explore possible influences of food web processes. Concentrations of the three most abundant PBDE homolog groups (tetra-, penta-, and hexa-PBDEs) were, for the most part, higher in Great Lakes and Lake Champlain fish compared with fish from other systems. The Canadian Federal Environmental Quality Guideline for the penta-homolog was exceeded in 70% of the fish examined. However, virtually no guideline exceedances were found for other congeners. In general, PBDE-47 (a representative lower brominated congener) was significantly and positively correlated with fish length, weight, age, lipid content, and stable isotopes of nitrogen and carbon. Significant differences in the slopes of the PBDE-47/covariate relationships between sites prevented concentrations from being adjusted using an analysis of covariance (ANCOVA). However, plots showed that elevated concentrations of PBDE-47 in Great Lakes and Lake Champlain fish remained after accounting for the influence of covariates. In contrast, for PBDE-183 (a representative higher brominated congener), the relationships between fish concentrations and covariates were not consistent, which could be a result of biotransformation being more important in controlling its bioaccumulation. The data from the current study show an overall disconnect between fish PBDE concentrations and likely loadings, which may be caused by differences in food web processes between systems. Continued long-term fish contaminant monitoring is needed to evaluate potential risk to fish and their consumers. However, we also recommend sediment sampling and focused food web studies to provide information on PBDE inputs to the systems and mechanisms of biomagnification, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".