Microplastic particles observed in multiple tissues of lake ontario sportfish
Bibliographic record
Abstract
Microplastics are a pervasive environmental contaminant, cycling through planetary cycles and food webs – leading to concerns regarding exposure and risk in humans. Here, we explore exposure and potential risks to humans by quantifying and characterizing microplastics in sportfish generally caught for human consumption. We sampled six species of fish from Humber Bay in Lake Ontario near Toronto, and examined their gastrointestinal (GI) tracts and fillets for the presence of microplastics and other anthropogenic particles. We also test hypotheses about bioaccumulation and biomagnification beyond the gut, and explore the data to look at patterns that may help understand mechanisms for translocation. We observed anthropogenic particles in both tissues of all fish sampled; fish had a mean of 147.9 particles (SD +/- 233.99), with the total number of particles in a single fish as high as 1508. The total particles per individual fish were significantly different among species (p ¡ 0.05), with the most plastic in Brown Bullhead fish (mean 299.9 particles; SD +/- 422.4) and the least in Largemouth Bass (mean 37.7 particles; SD +/- 23.9). Fish GI tracts had a mean of 92.6 particles/fish (SD +/- 225.96), and fish fillets had a mean of 55.73 particles/per fish (SD +/- 61.21). The number of particles observed in the GI tract was not correlated with the number of particles in the fillet, suggesting that the particles in the gut are transitory and not a predictor of the amount in other tissues. The particularly high number of particles that were observed to contaminate the fillets highlights the need for further research to better understand the mechanisms and consequences of the movement of microplastics within fish. Further, the presence of microplastics in tissues consumed by humans (the fillets), indicates that fish contribute to microplastic burdens in humans. Also see: https://micro2022.sciencesconf.org/423786/document
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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 | 0.001 |
| 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".