Toxicity assessment of environmental MPs and NPs and polystyrene NPs on the bivalve Corbicula fluminea using a multi-marker approach
Bibliographic record
Abstract
Small plastic particles, microplastics (MPs) and nanoplastics (NPs) represent a major threat in aquatic environments. Freshwater organisms are exposed to MPs and NPs, particularly in industrial and urban areas. The present study aimed to compare the toxicity between polystyrene NPs (PS NPs) and environmental microplastics (ENV MPs) and nanoplastics (ENV NPs) generated from macro-sized debris collected in the Garonne River on the freshwater bivalve C. fluminea. The organisms were exposed to the different plastic particles at three environmentally relevant concentrations: 0.008, 10, and 100 μg L−1 for 21 days. The biological responses of organisms were assessed using a multi-biomarker approach from the sub-individual to the individual level. The results demonstrated that: i) ENV NPs triggered more effects on detoxification processes and immune response, confirming that using manufactured NPs for laboratory exposure can lead to misleading conclusions on the risks posed by plastic particles; ii) effects of ENV MPs were less marked than ENV NPs, emphasizing the importance of testing a size continuum of plastic particles from NPs to MPs; iii) some effects were only observed for the low and/or intermediate concentrations tested, underlining the importance of using environmentally relevant concentrations. In light of these results, laboratory studies should be continued by exposing aquatic species to environmental MPs and NPs. The properties of these particles have to be characterized for a better risk assessment of environmental plastic particles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".