Dietary Uptake Patterns Affect Bioaccumulation and Biomagnification of Hydrophobic Organic Compounds in Fish
Bibliographic record
Abstract
Biomagnification of hydrophobic organic compounds (HOCs) increases the eco-environmental risks they pose. Here, we gained mechanistic insights into biomagnification of deuterated polycyclic aromatic hydrocarbons (PAHs- d 10 ) in zebrafish with carefully controlled water (ng L –1 ) by a passive dosing method and dietary exposures using pre-exposed Daphnia magna and fish food. A new bioaccumulation kinetic model for fish was established to take into account discrete dietary uptake, while the frequently used model regards dietary uptake as a continuous process. We found that when freely dissolved concentrations of the PAHs- d 10 were constant in water, the intake amount of the PAHs- d 10 played an important role in affecting their steady-state concentrations in zebrafish, and there was a peak concentration in zebrafish after each dietary uptake. Moreover, considering the randomness of predation, the Monte Carlo simulation results showed that the probabilities of biomagnification of the PAHs- d 10 in zebrafish increased with their dietary uptake amount and frequency. This study indicates that in addition to the well-known lipid–water partitioning, the bioaccumulation of HOCs in fish is also a discontinuous kinetic process caused by the fluctuation of HOC concentration in the gastrointestinal tract as a result of the discrete food ingestion. The discontinuity and randomness of dietary uptake can partly explain the differences among aquatic ecosystems with respect to biomagnification for species at similar trophic levels and provides new insight for future analysis of experimental and field bioaccumulation data for fish.
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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".