Governance Strategies for Mitigating Microplastic Pollution in the Marine Environment: A Review
Bibliographic record
Abstract
Threats emerging from microplastic pollution in the marine environment have received much global attention. This review assessed sources, fate, and impacts of microplastics in marine ecosystems and identified gaps. Most studies document the ubiquity of microplastics and associated environmental effects. Effects include impacts to marine ecosystems, risks to biodiversity, and threats to human health. Microplastic leakage into marine ecosystems arises from plastic waste mismanagement and a lack of effective mitigative strategies. This review identified a scarcity of microplastics’ mitigation strategies from different stakeholders. Lack of community involvement in microplastic monitoring or ecosystem conservation exists due to limited existence of citizen science and stakeholder co-management initiatives. Although some management strategies exist for controlling effects of microplastics (often implemented by local and global environmental groups), a standardized management strategy to mitigate microplastics in coastal areas is urgently required. There is a need to review policy interventions aimed at plastic reduction in or near coastal ecosystems and evaluate their effectiveness. There is also a need to identify focal causes of microplastic pollution in the marine environment through further environmental research and governance approaches. These would extend to creating more effective policies as well as harmonized and extended efforts of educational campaigns and incentives for plastic waste reduction while mandating stringent penalties to help reduce microplastic leakage into the marine environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".