Direct and indirect effects of different types of microplastics on freshwater prey (Corbicula fluminea) and their predator (Acipenser transmontanus)
Bibliographic record
Abstract
We examined whether environmentally relevant concentrations of different types of microplastics, with or without PCBs, directly affect freshwater prey and indirectly affect their predators. Asian clams (Corbicula fluminea) were exposed to environmentally relevant concentrations of polyethylene terephthalate (PET), polyethylene, polyvinylchloride (PVC) or polystyrene with and without polychlorinated biphenyls (PCBs) for 28 days. Their predators, white sturgeon (Acipenser transmontanus), were exposed to clams from each treatment for 28 days. In both species, we examined bioaccumulation of PCBs and effects (i.e., immunohistochemistry, histology, behavior, condition, mortality) across several levels of biological organization. PCBs were not detected in prey or predator, and thus differences in bioaccumulation of PCBs among polymers and biomagnification in predators could not be measured. One of the main objectives of this study was to test the hypothesis that bioaccumulation of PCBs would differ among polymer types. Because we could not answer this question experimentally, a bioaccumulation model was run and predicted that concentrations of PCBs in clams exposed to polyethylene and polystyrene would be greater than PET and PVC. Observed effects, although subtle, seemed to be due to microplastics rather than PCBs alone. For example, histopathology showed tubular dilation in clams exposed to microplastics with PCBs, with only mild effects in clams exposed to PCBs alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".