Analysis of Microplastic Sources in the South Saskatchewan River and Selected Saskatoon Storm Ponds
Bibliographic record
Abstract
Due to their widespread presence in all ecosystems, microplastics have been classified as significant persistent environmental contaminants. Aquatic organisms' reproductive cycles, access to energy, and growth may all be adversely affected by microplastics in water bodies. As a result, it is important to identify the origins and quantities of microplastic in significant Saskatchewan waterways, particularly the South Saskatchewan River, which supplies water to more than 50% of the province's residents for a variety of uses. In this study, we used Raman micro-spectroscopy to examine the compositions and loadings of microplastics in samples taken from three storm ponds in the City of Saskatoon and seven sites along the South Saskatchewan River. Microplastics were identified in all river and storm pond samples with the mean concentrations of 4.43 ± 2.88 m-3 and 6.44± 3.62 m-3 respectively. Although the small sample size and large variability in the mean microplastics concentrations between samples limits meaningful statistical analysis, our results suggest that the mean microplastics load at Miry creek (12.00 ± 9.12 m-3) is higher compared to 3.18 ± 3.00 m-3 for all other river samples, indicating Diefenbaker dam may be acting as a sink for microplastics along the waterways. Fibers dominate the morphology class of microplastics recovered, while polymers from polyethylene terephthalate (PET), polypropylene (PP), and polystyrene (PS) were the major microplastics chemically identified among the samples. Particle contributions from both dyed and undyed natural fibers were also analyzed. This study represents the first exploration of microplastic levels in the South Saskatchewan River and selected Saskatoon’ storm ponds, thereby improving our understanding of this pervasive environmental contamination 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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".