Microplastics in aquatic systems: A review of occurrence, monitoring and potential environmental risks
Bibliographic record
Abstract
Plastic particles of microscopic scale are present in the aquatic environment, especially at lower trophic trophic levels where the number of microplastics (MPs) ingested per gram wet weight is greater than in higher trophic levels. The presence of microplastics (MPs) in water bodies is caused by anthropogenic activities and waste disposal negligence such as disposal waste, disposal waters, industry, agriculture, fishing, ship traffic, and environmental factors, which have been monitored in remote locations by bioindicators and tracking tools such as numerical modeling and life cycle inventories. Our review process shows that more studies are conducted in the northern hemisphere, and most of the analyzed MPs are either Polyethylene (PE), Polypropylene (PP), or Polystyrene (PS). Moreover, several papers report potential adverse effects on the biota can be disturbances in feeding, mobility, and reproduction that may cause lethal or sub-lethal consequences. Thus, to reduce the environmental impact and the effects on species exposed to microplastic particles we suggest research that helps in the establishment of limits of occurrence of these materials according to their physical-chemical properties, uniform measuring standards, and their toxicity to the environment to promote legislation for the control and mitigation this contamination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".