Microplastics and Anthropogenic Particles in Recreationally Caught Freshwater Fish from an Urbanized Region of the North American Great Lakes
Bibliographic record
Abstract
BACKGROUND: Microplastics are a pervasive contaminant cycling through food webs-leading to concerns regarding exposure and risk to humans. OBJECTIVES: We aimed to quantify and characterize anthropogenic particle contamination (including microplastics) in fish caught for human consumption from the Humber Bay region of Lake Ontario. We related quantities of anthropogenic particles to other factors (e.g., fish size) that may help in understanding accumulation of microplastics in fish. METHODS: A total of 45 samples of six fish species collected from Humber Bay in Lake Ontario near Toronto, Ontario, Canada, were examined for anthropogenic particles in their gastrointestinal (GI) tracts and fillets. Using microscopy and spectroscopy, suspected anthropogenic particles were identified and characterized. RESULTS: particles. DISCUSSION: Our findings suggest that consumption of recreationally caught freshwater fish can be a pathway for human exposure to microplastics. The elevated number of particles observed in fish from Humber Bay highlights the need for large-scale geographic monitoring, especially near sources of microplastics. Currently, it is unclear what the effects of ingesting microplastics are for humans, but given that recreationally caught freshwater fish are one pathway for human exposure, these data can be incorporated into future human health risk assessment frameworks for microplastics. https://doi.org/10.1289/EHP13540.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".