Microplastics as vectors of environmental contaminants: Interactions in the natural ecosystems
Bibliographic record
Abstract
Microplastics (MPs) have recently been recognized as potential pollutants and carriers for pathogens in marine, freshwater, and terrestrial environments. They can carry microbial pathogens, hydrophobic organic compounds, persistent organic pollutants, and heavy metals on the surface of these particles leading to unwanted effects on living organisms. Their harmful effects on aquatic and terrestrial organisms have been well established. This includes damage to cell membranes, tissues, and physiological processes. Further, being highly persistent in natural ecosystems, they can amass in various environments over long periods of time. Their accumulation of MPs substantially depends on plastic usage and its management policies around the world; therefore, a closer look at the potential hazards and build-up of MPs is timely. Also, it is crucial to understand the significance of currently established methods on their removal from the ecosystem including activated sludge treatment, coagulation and flocculation, and removal via membrane bioreactors. Among them, constructed wetlands are considered an environmentally friendly technology with ease in operation and low cost that could efficiently remove MPs from wastewater. This article specifically compiles existing literature on the current understanding of MPs in the environment, their role as environmental carriers, interactions in natural ecosystems, the recent developments in their research, and the way forward.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".