Prevalence of microplastics and anthropogenic debris within a deep-sea food web
Bibliographic record
Abstract
Microplastic particles (<5 mm) are ubiquitous throughout global marine ecosystems, including the deep sea. Ingestion of microplastics and other anthropogenic microparticles is reported in diverse marine taxa across trophic levels. Trophic transfer, or the movement of microplastics across trophic levels, is reported in laboratory studies but not yet widely measured in marine food webs. The Monterey Bay submarine canyon ecosystem contains a well-studied, known deep-sea food web in which to examine the trophic fate of microplastics. We measured microplastic abundance across 17 genera spanning approximately 5 trophic levels and a diversity of feeding behaviors. Samples were collected using remotely operated vehicles and oblique midwater trawls, and gut contents of all individuals examined (n = 157) were analyzed for microplastic abundance and other anthropogenic particles greater than 100 µm using stereo microscopy. Microparticles were analyzed with Raman spectroscopy to confirm material type. Anthropogenic particles were found in all genera examined, across crustacean, fish, mollusk, and gelatinous organisms, in amounts ranging from 0 to 24 particles per individual. There was no significant relationship between microplastic amount and fish trophic level, suggesting that the trophic transfer of microparticles is not occurring. Body size was positively correlated with microplastic abundance across all taxa. The fish genus Scomber sp. drove this relationship, suggesting higher microparticle abundance in mobile individuals with broad horizontal distributions. Future work should examine physiological pathways for microplastic transport within organisms (e.g. excretion, accumulation on gills, internal translocation of particles) and between organisms within shared habitats to more fully understand the fate of microplastics within aquatic food webs.
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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 | 0.001 |
| 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".