Microplastics in Arctic invertebrates: status on occurrence and recommendations for future monitoring
Bibliographic record
Abstract
Few studies have been published on the occurrence and distribution of microplastics (MPs) in invertebrates from the Arctic. We still need to develop harmonized methods to enable good comparison between studies taking into account recovery rates, size ranges, shapes, and polymer types. Here, we review studies on MPs in invertebrates from the Arctic and present suggestions on sampling protocols and potential indicator species. Since information on MPs in Arctic invertebrates is vastly lacking, we recommend to at least include suspension feeding bivalves like mussels in monitoring programmes to function as indicator species in the Arctic. Mussels have also been suggested as indicator species for MP monitoring in coastal regions further south. Although we recognize the challenge with particle selection and egestion in mussels as well as the relatively low concentrations of MPs in Arctic waters, uptake levels seem to represent recent exposures. More research is needed to understand these selection processes and how they affect the bioaccumulation processes. Future research should include studies on whether different functional groups of invertebrates have different exposures to MPs, e.g., if there are differences between sessile versus motile species or different feeding strategies. More knowledge on monitoring strategies for pelagic and benthic species is needed.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".