Fate of PBDEs in Juvenile Lake Trout Estimated Using a Dynamic Multichemical Fish Model
Bibliographic record
Abstract
Biotransformation half-lives (HL) and gut absorption efficiencies (GAE) of PBDE congeners in fish are poorly known and challenging to quantify experimentally. These values are needed in order to accurately assess their food web dynamics, and in turn, for policy development We recently developed a multichemical aquatic food web model, which was used to estimate HL of four PBDE congeners in a simple Arctic food web. However, an application of this model to more complex food webs would dramatically increase the uncertainties in the results due to the large number of unknowns that would need to be considered simultaneously. As such, an in-depth analysis of possible HL and GAE of additional PBDE congeners at the scale of individual fish species would facilitate model application to more complex food webs. For this purpose, we developed a fugacity-based dynamic multichemical fish model and applied it to previously published experimental laboratory data. The model was calibrated by maximizing correspondence between the modeled and observed concentrations for each of the thirteen congeners at two dietary concentrations in juvenile lake trout (Salvelinus namaycush) during uptake and depuration phases mainly by varying HL and GAE. A robust parametrization and calibration procedure gave us confidence in our back-calculated congener-specific HL of 42-420 days and GAE of 20-45%. These values can be used as a starting point for model applications to natural fish populations. The fate/transport results suggest that not only loss of PBDE congeners via degradation, but also input through biotransformation of higher brominated congeners, should be accounted for in order to accurately portray dynamics of PBDEs in 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 | 0.001 | 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".