Seafood consumption and PBDE exposure in Newfoundland, Canada: Insights into the sustainability of local fisheries and public health
Bibliographic record
Abstract
Coastal communities like Newfoundland depend on seafood for sustenance, economy, and cultural identity. However, shifting consumption patterns threaten the sustainability of local fisheries and pose public health risks, particularly from persistent organic pollutants like polybrominated diphenyl ethers (PBDEs). This study investigates the socioeconomic factors and dietary habits influencing seafood choices in Newfoundland and explores the potential health risks associated with PBDE through dietary exposure. The results reveal that older generation tend to favor locally sourced seafood species, while the younger generation often choose non-local seafood due to affordability and availability, creating challenges for the long-term sustainability of local fisheries and marine ecosystems. Further, the estimated dietary chronic exposure to PBDEs in local seafood, such as cod, exhibits the highest concentrations of BDE-47 (314 ng/kg bw/year) and BDE-99 (9.88 ng/kg bw/year). While these levels are below safety thresholds, chronic low-dose exposure poses potential risks, especially for vulnerable populations. This study highlights the need for integrated environmental management strategies to promote local, sustainable seafood consumption, monitor contaminants, and protect public health in coastal communities.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".