Comparison of the Toxicokinetics and Bioaccumulation Potential of Mercury and Polychlorinated Biphenyls in Goldfish (<i>Carassius auratus</i>)
Bibliographic record
Abstract
Both mercury (Hg) and polychlorinated biphenyls (PCBs) demonstrate food web biomagnification in aquatic ecosystems, yet their toxicokinetics have not been simultaneously contrasted within a common fish species. This study quantifies uptake and elimination rates of Hg and PCBs in goldfish. Fish were exposed to contaminated food containing PCBs and Hg to determine dietary chemical assimilation efficiencies (AEs) and elimination coefficients (ktot). To test first-order kinetics, three exposure regimes were established by varying the proportion of contaminated fish incorporated into the food. Dietary AEs were 98 ± 10, 75 ± 12, and 40 ± 9% for MeHg, THg, and PCBs, respectively. The ktot values were 0.010 ± 0.003 and 0.010 ± 0.002 day(-1) for THg and MeHg, respectively. No significant differences were found in ktot among the dosing levels for either THg or MeHg, confirming that Hg elimination is a first-order process. For PCB, ktot ranged from 0.007 to 0.022 day(-1) and decreased with an increase in hydrophobicity. This study revealed that Hg had an AE higher than that of PCBs, while the ktot of Hg was similar to those measured for the most hydrophobic PCBs. We conclude that Hg has a bioaccumulation potential in goldfish 118% higher than the highest PCB BMF observed for congeners with a log KOW of >7.
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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) | 0.000 | 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".