Black Carbon Inclusive Multichemical Modeling of PBDE and PCB Biomagnification and -Transformation in Estuarine Food Webs
Bibliographic record
Abstract
Bioavailability and bioaccumulation of polybrominated diphenylethers (PBDEs) are affected by adsorption on black carbon (BC) and metabolism in biota, respectively. Recent studies have addressed these two processes separately, illustrating their importance in assessing contaminant dynamics. In order to properly examine biomagnification of polychlorinated biphenyls (PCBs) and PBDEs in an estuarine food-web, here we set up a black carbon inclusive multichemical model. A dual domain sorption model, which accounted for sorption to organic matter (OM) and black carbon (BC), was used to estimate aqueous phase concentrations from the measured chemical concentrations in suspended solids. We adapted a previously published multichemical model that tracks the movement of a parent compound and its metabolites in each organism and within its food web. First, the model was calibrated for seven PCB congeners assuming negligible metabolism. Subsequently, PBDE biomagnification was modeled, including biotransformation and bioformation of PBDE congeners, keeping the other model parameters the same. The integrated model was capable of predicting trophic magnification factors (TMF) within error limits. PBDE metabolic half-lives ranged 21-415 days and agreed to literature data. The results showed importance of including BC as an adsorbing phase, and biotransformation and bioformation of PBDEs for a proper assessment of their dynamics in aquatic systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".