Plastic pollution in terrestrial ecosystems: Current knowledge on impacts of micro and nano fragments on invertebrates
Bibliographic record
Abstract
The increasing accumulation of small plastic particles, in particular microplastics (>1 µm to 5 mm) and nanoplastics (< 1 µm), in the environment is a hot topic in our rapidly changing world. Recently, studies were initiated to better understand the behavior of micro- and nanoplastics (MNP) within complex matrices like soil, as well as their characterization, incorporation and potential toxicity to terrestrial biota. However, there remains significant knowledge gaps in our understanding of the wide-extent impacts of MNP on terrestrial invertebrates. We first summarized facts on global plastic pollution and the generation of MNP. Then, we focused on compiling the existing literature examining the consequences of MNP exposure in terrestrial invertebrates. The diversity of investigated biological endpoints (from molecular to individual levels) were compiled to get a better comprehension of the effects of MNP according to different factors such as the shape, the polymer type, the organism, the concentration and the exposure duration. The sublethal effects of MNP are acknowledged in the literature, yet no general conclusion was drawn as their impacts are highly dependent on their characteristic and experimental design. Finally, the synthesis highlighted some research gaps and remediation strategies, as well as a protocol to standardize ecotoxicological studies.
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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 | 0.002 | 0.003 |
| 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) | 0.004 | 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".