Strategies for Monitoring and Reducing Microplastic Pollution in Oceans
Bibliographic record
Abstract
Microplastics have become an emerging pollutant in the global marine environment and have attracted widespread attention. Based on the research progress at home and abroad in the past five years, this study systematically reviews the source, distribution, ecological environment and health impact of marine microplastic pollution, and discusses monitoring and governance strategies. The study found that microplastic particles produced by the decomposition of plastic waste are widely present in the ocean, posing a potential threat to plankton, benthic organisms, and human health of microplastics ingested through the food chain. In response to this global threat, countries and international organizations have successively carried out microplastic monitoring projects, developed a series of monitoring technologies, and formulated policies and regulations to reduce plastic waste emissions. At the engineering and technical level, the exploration of recycling and new cleaning technologies has made continuous progress. At the same time, public participation and educational publicity have played an important role in the control of plastic pollution. Therefore, formulating comprehensive strategies, monitoring, controlling and reducing microplastic pollution has become a key issue in achieving sustainable development of the marine ecological 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".