Towards a management strategy for microplastic pollution in the Laurentian Great Lakes—ecological risk assessment and management (part 2)
Bibliographic record
Abstract
Over the past decade, plastic pollution has been documented throughout the Laurentian Great Lakes basin. Here, we demonstrate the application of published ecological risk assessment and management frameworks for microplastics in aquatic environments by comparing proposed risk thresholds derived using toxicity data from both freshwater and marine species to local monitoring data. Our results suggest that there may be measurable risks from microplastics to aquatic communities in parts of the Great Lakes where current concentrations are relatively high. For example, concentrations in 89% of surface water samples collected across the region exceed the proposed risk thresholds for food dilution toxicity. However, concentrations in all sediment samples remain below the proposed risk thresholds. Accordingly, we suggest that an appropriate and necessary next step for management may include convening a working group of local experts to develop an ecological risk assessment and management framework for the region comprising risk thresholds for microplastics in surface water and sediment. Ultimately, microplastic pollution should be addressed in the Great Lakes Water Quality Agreement to ensure coordinated and sustained efforts are taken by the governments of Canada and the United States to reduce their release and impact.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".