Microplastics Flowing into Lake Winnipeg: Densities, Sources, Flux, and Fish Exposures
Bibliographic record
Abstract
Microplastics (plastic particles < 5.0 mm in diameter) have been detected in freshwater ecosystems worldwide. Recently, surface concentrations of microplastics in Lake Winnipeg, Manitoba were shown to be comparable to those observed in Lake Erie, Ontario, despite large differences between the lakes in terms of population density and industrial activity. To better understand potential sources of microplastics into Lake Winnipeg, two inflowing tributaries (the Red and Assiniboine rivers) and the lake outflow (the Nelson River) were sampled for microplastics. To determine the role of wastewater treatment plants in contributing to microplastic pollution, microplastic densities upstream and downstream of wastewater treatment plants in the city of Winnipeg were compared. Finally, to determine the bioavailability of microplastics to fishes, we evaluated the presence of microplastics in the gastrointestinal tracts of two fish species, common carp (Cyprinus carpio) and sauger (Sander canadensis) collected from the Red River. Microplastics in the Red and Assiniboine rivers were comparable to those from Great Lake tributaries, but were elevated four to six times relative to concentrations observed in the Nelson River, suggesting significant losses to settling in Lake Winnipeg. On average, densities of microplastics downstream of wastewater treatment plants were elevated, and a significant correlation was observed between standardized daily effluent discharge from Winnipeg and river flux of microplastics/m2/s. On average, sauger were found to contain one microplastic particle and carp were found to contain seven microplastics within their gastrointestinal tracts. The number of particles ingested did not appear to affect body condition of fish collected in this study.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".