Microplastics in the benthic fish from the Canadian St. Lawrence River and Estuary: Occurrence, spatial distribution and ecological risk assessment
Bibliographic record
Abstract
Microplastic contamination in the St. Lawrence River and Estuary (SLRE), Canada, poses potential risks to aquatic species. However, limited understanding of microplastic contamination in benthic fish, potentially more vulnerable than pelagic species, impedes effective risk assessment in this crucial ecosystem. This study addressed knowledge gaps by analyzing microplastics in the gastrointestinal tracts (GIT) and gills of Channel Catfish (Ictalurus punctatus) and Atlantic Tomcod (Microgadus tomcod) in the SLRE. Forty-two fish from ten stations were examined using KOH digestion, density separation, wet-peroxidation, and spectroscopy. Results indicated an average abundance of 3.0 ± 0.4 (mean ± SE) microplastic particles per individual fish. Most detected particles were small microplastics (<809 μm) and fibers, with blue and transparent colors. Major polymers identified included polyethylene terephthalate and polyethylene. While catfish showed higher microplastic abundances per individual than tomcod, data based on GIT weight do not support microplastic biomagnification in this predator-prey relationship. Catfish from downstream of Québec City showed elevated levels of microplastics and more variations in their characteristics compared to average abundance found from a site located 50 km upstream. Urban activity may increase microplastic accumulation in downstream benthic fish and others. This highlights the need for further studies on the migratory capacities of fish species. Ecological risk assessment revealed medium to high-risks for the catfish stations close to the Québec City due to the prevalence of smaller microplastics <809 μm and highly toxic polymers (polymethyl methacrylate, polyvinylchloride, polyurethane, acrylonitrile butadiene styrene). This study provides a baseline for monitoring plastic pollution in the SLRE fish and assessing ecological risks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".