Surface Water Microplastics in the St. Lawrence River and Estuary in Canada
Bibliographic record
Abstract
Microplastics (MPs) are synthetic or semisynthetic polymers that are widely distributed throughout most ecosystems and have the potential to be harmful to living organisms. In this study, we assessed the MP fraction in the top 40 cm of surface water in response to varying salinity levels at 11 distinct sites across the St. Lawrence River and Estuary (SLRE). We employed two sampling nets of different mesh size to collect MPs (100 and 300 µm). These nets were simultaneously towed in parallel from a vessel during three separate sampling events at each designated site. Filtrates collected from these samples underwent analysis of plastic fibers, fragments and spheres utilizing Fourier Transform Infrared Spectroscopy (FTIR). Data unequivocally confirmed the presence of MPs at 100% of the sites sampled within the SLRE. The most abundant categories of MPs identified were the fibers, followed by fragments and spheres. The FTIR analysis revealed the predominant materials to be polyester, polyethylene, polypropylene, nylon, and polystyrene. Notably the findings also suggest MPs are more likely aggregating when salinity increases. This work offers valuable insights into the distribution and behavior of MPs contributing to the preservation and management of water resources.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".