Microplastic biomonitoring studies in aquatic species: A review & quality assessment framework
Bibliographic record
Abstract
A large body of literature exists demonstrating the exposure, uptake, and presence of micro- and nanoplastic particles (MNPs) within marine biota. Despite this, there remain challenges in synthesizing these studies in a consistent and reliable manner which can support technology, regulatory, and policy decision-making. The most significant challenge is a lack of guidance to assess and integrate study reliability (objective quality) and relevance (ability to answer a specific question). The purpose of this study is twofold - first, to critically review and apply existing frameworks to an expanded body of literature. Second, to propose meaningful criteria to further assess study utility as it applies to the use of biomonitoring data to reliably quantitate (1) relationships between external and internal (biota) concentrations of MNPs, (2) differences among organisms, species, and/or regions, and (3) utility of species as effective biomonitors for MNPs in the marine environment. A critical screening of 409 biomonitoring studies published between 2017 and 2022 was carried out using previously established reliability criteria. Studies included 1243 unique species and 1954 distinct research units. Two gateway criteria were proposed to assess the relevance and utility for biomonitoring and risk assessment: polymer identification and the inclusion of an environmental sample (water or sediment). In comparison to previously published systematic reviews, the general quality of study design is improving with time. Nonetheless, deficiencies impacting the relevance and reliability are still common. In total, only 8 % of all studies passed the screening and gateway criteria, and scored ≥50 % in reliability, suggesting that studies which provide sufficient rigor and data to support confident quantitative analysis and decision-making remain limited. A series of recommendations for journals, reviewers, and researchers are proposed to increase the utility and impact of future studies, particularly as they are applied within the context of ecological risk assessment and decision-making.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".