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Record W4415721791 · doi:10.5376/ijms.2025.15.0022

Strategies for Monitoring and Reducing Microplastic Pollution in Oceans

2025· article· W4415721791 on OpenAlexvenueno aff
Wenzhong Huang, Xuelian Jiang

Bibliographic record

VenueInternational Journal of Marine Science · 2025
Typearticle
Language
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsPlastic pollutionHuman healthMarine pollutionPollutionMarine debrisPlastic wastePollution prevention

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.268
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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