Microplastics: understanding the interaction with the food web and potential health hazards
Bibliographic record
Abstract
Microplastics have become a universal environmental contaminant, penetrating marine and freshwater ecosystems and presenting significant adverse effects on aquatic life and human health. In aquatic organisms, microplastics can cause physical harm, disrupt feeding and reproductive behaviours, and carry toxic chemicals that intensify their impact. For humans, the ingestion of microplastics through contaminated seafood and water raises concerns about long-term health complications, including inflammation, endocrine disruption, and exposure to harmful additives and pollutants associated with microplastics. The exploration of the origin of microplastics, their transport, and distribution at various trophic levels of the food web has become an imperative environmental concern. From filter-feeding zooplankton to predatory fish, microplastics are ingested and assimilated, with the potential for bioaccumulation and biomagnification along the food chain. Moreover, their small size and widespread dispersal make them particularly challenging to mitigate or eliminate from the environment. The present review underscores the necessity for ongoing research to fully elucidate the mechanisms and consequences of microplastic interactions within the food web. Enhanced understanding of these dynamics is crucial for developing effective mitigation strategies and regulatory policies aimed at reducing microplastic pollution and protecting ecosystem and human health.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".