What Does the “Trojan Horse” Carry? The Pollutants Associated with Microplastics/Nanoplastics in Water Environments
Bibliographic record
Abstract
Plastic additives are intensively used in the plastic industry. Namely, plasticizers, flame retardants, stabilizers, and antioxidants have raised significant environmental concerns. These additives, characterized by relatively high toxicity and bioaccumulation rates, pose substantial risks to human health. When plastics break down into smaller fragments (i.e., micro/nanoplastics (MNPs)), these additives can be released into aquatic environments, where they may interact with other pollutants through various mechanisms, and multiple factors can affect such interactions. This can influence the occurrence of MNP-associated pollutants in water environments and further impact the environment and human health. Although MNP additives and their associated pollutants pose significant risks, research on their behavior and impacts remains limited. This review maps out the current understanding of MNP additives and associated pollutants, and identifies critical knowledge gaps, setting a foundation for future research directions that will further unravel the complexities of MNPs in water environments and their broader implications.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".