Levels and composition of microplastics and microfibers in the South Saskatchewan River and stormwater retention ponds in the City of Saskatoon, Canada
Bibliographic record
Abstract
Abstract In recent decades, contamination of the environment with microplastics and microfibers has been recognized as a pervasive and ubiquitous issue of global concern. While much research in this field has been undertaken in marine environments, more recent studies have identified rivers as major conveyors of plastic pollution from terrestrial into marine systems. However, reports on the levels and composition of microplastic and microfiber contamination in rivers of the Canadian prairie region, specifically the South Saskatchewan River (SSR), are scarce, which leaves this vital source of water for societies and ecosystems in a vulnerable state. To fill this gap, we obtained samples from seven sites along the Saskatchewan portion of the SSR, as well as three stormwater retention ponds (SRP) in the city of Saskatoon during the spring, summer, and fall of 2020. We used optical and Raman microscopy to enumerate and characterize particles in these samples. Total levels of particles and fibers in all samples ranged from 32 to 116 particles m− 3. Most particles (approx. 77%) were natural fibers, while polymers accounted for the remaining 33%. Average microplastic levels were lower (3.18 ± 3 particles m− 3) downstream of Lake Diefenbaker, a large reservoir on the SSR, compared to upstream (12.0 ± 9 particles m− 3). Retention of microplastics in the reservoir could explain the lower mean microplastic concentration of 4.43 ± 3 particles m− 3 recorded in the SSR compared to mean concentrations of 26.2 ± 18 particles m− 3 reported in the North Saskatchewan River, which is not dammed. This study is among the first to describe microplastic and microfiber levels in the SSR and thereby helps improve our understanding of this pervasive environmental contaminant on the Canadian prairies.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".