Plastic Microbead Accumulation in our Freshwaters: North American Great Lakes Assessments and Perspectives
Bibliographic record
Abstract
Plastic microbeads, widely incorporated into personal care and cleansing products, have emerged as a pervasive contaminant in freshwater systems, including in North America. Historical estimates indicate that North American consumers alone contributed trillions of microbeads daily to municipal wastewater, with global usage reaching quadrillions per day. Despite regulatory actions such as Canada’s 2018 ban on microbeads in personal care products, these particles persist in the environment due to their resistance to degradation and continued release from unregulated sources, including industrial abrasives and certain cleaning agents. Studies across the Great Lakes, one of the world’s largest freshwater systems, have documented widespread microbead contamination in surface waters, sediments, and shorelines, highlighting their persistence and accumulation. This review synthesizes findings from key studies conducted between 2013 and 2017 to establish a pre-ban baseline of microbead distribution in the Great Lakes, and presents new data collected from 2018 to 2021 as a post-ban contamination assessment. The review emphasizes the unique challenges posed by microbeads within the broader context of microplastic pollution and underscores the critical role of polymer chemists and engineers in developing innovative materials and removal strategies to mitigate future contamination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".