Emerging investigator series: microplastic sources, fate, toxicity, detection, and interactions with micropollutants in aquatic ecosystems – a review of reviews
Bibliographic record
Abstract
Hundreds of review studies have been published focusing on microplastics (MPs) and their environmental impacts. With the microbiota colonization of MPs being firmly established, MPs became an important carrier for contaminants to step inside the food web all the way up to humans. Thus, the continuous feed of MPs into the ecosystem has sparked a multitude of scientific concerns about their toxicity, characterization, and interactions with microorganisms and other contaminants. The reports of common subthemes have agreed about many findings and research gaps but also showed contradictions about others. To unravel these equivocal conflicts, we herein compile all the major findings and analyze the paramount discrepancies among these review papers. Furthermore, we systematically reviewed all the highlights, research gaps, concerns, and future needs. The covered focus areas of MPs' literature include the sources, occurrence, fate, existence, and removal in wastewater treatment plants (WWTPs), toxicity, interaction with microbiota, sampling, characterization, data quality, and interaction with other co-contaminants. This study reveals that many mechanisms of MPs' behavior in aquatic environments like degradation and interaction with microbiota are yet to be comprehended. Furthermore, we emphasize the critical need to standardize methods and parameters for MP characterization to improve the comparability and reproducibility of the incoming research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".