Effect of Microplastic Amendment to Food on Diet Assimilation Efficiencies of PCBs by Fish
Bibliographic record
Abstract
Diet assimilation efficiencies (AEs) of polychlorinated biphenyls (PCBs) absorbed to microplastics and food were determined in goldfish ( Carassius auratus ). Microplastics were spiked with 14 environmentally rare PCBs and incorporated into fish pellets previously spiked with a technical PCB mixture (Aroclor 1254). Five diet treatments were created having microplastic contents of 0, 5, 10, 15, 20, and 25% and fed to fish within 24 h of the diet creation. Fish from each treatment were fed a microplastic amended food pellet and PCB AEs were determined by mass balance. Microplastic-associated PCBs had lower AEs (geomean 13.36%) compared to food matrix-associated PCBs (geomean 51.64%). There were interactions between PCB AEs and the microplastic content of the diet. PCBs affiliated with microplastics became more bioavailable with increasing microplastic content of food while food matrix-associated PCB bioavailability declined when microplastic contents exceeded 5%. Despite controlling for microplastic-food contact time, there was some evidence for redistribution of lower K OW food matrix-associated PCBs onto microplastics causing a decrease in their AE relative to nonplastic and low plastic containing diets. The low bioavailability of microplastic-associated PCBs observed in the present study provides further support to indicate that microplastics are unlikely to increase POPs bioaccumulation by fish in aquatic systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".