Little evidence for bioaccumulation or biomagnification of microplastics in a deep-sea food web
Bibliographic record
Abstract
Microplastic contamination is documented in marine organisms, but little is known about bioaccumulation or biomagnification of microplastics, especially in tissues external to the gastro-intestinal (GI) tract. The objective of this work was to explore microplastic contamination in GI tracts and other tissues (abdomen and tail in crustaceans; mantle in cephalopods; fillets in fishes) of species at different trophic levels sampled in a deep-sea food web in Monterey Bay, CA, USA. The species included are tuna crab Pleuroncodes planipes , market squid Doryteuthis opalescens , northern lampfish Stenobrachius leucopsarus , chub mackerel Scomber japonicus , California halibut Paralichthys californicus , and Chinook salmon Oncorhynchus tshawytscha . After chemical digestion, microplastics in GI tracts were quantified and identified to material type using μ-Raman spectroscopy and in other tissues using pyrolysis-GC/MS. The concentrations of microplastics in GI tracts were significantly different among species, and microplastic contamination was dominated by microfibers. The concentrations of microplastics (mainly polyethylene and polyvinyl chloride) in other tissues also varied among species. A significant positive correlation between body size and plastic concentration in other tissues was observed for halibut only, suggesting bioaccumulation may not be ubiquitous. The trophic magnification factor for microplastics beyond the GI tract was <1, suggesting that biomagnification is not occurring in tissues. However, we did observe evidence for biomagnification of microplastics in the GI tracts. Future studies are needed to better understand these patterns and the mechanisms for translocation, bioaccumulation, and biomagnification of microplastics in aquatic organisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".