Development of a Multichemical Food Web Model: Application to PBDEs in Lake Ellasjøen, Bear Island, Norway
Bibliographic record
Abstract
A multichemical food web model has been developed to estimate the biomagnification of interconverting chemicals in aquatic food webs. We extended a fugacity-based food web model for single chemicals to account for reversible and irreversible biotransformation among a parent chemical and transformation products, by simultaneously solving mass balance equations of the chemicals using a matrix solution. The model can be applied to any number of chemicals and organisms or taxonomic groups in a food web. The model was illustratively applied to four PBDE congeners, BDE-47, -99, -100, and -153, in the food web of Lake Ellasjøen, Bear Island, Norway. In Ellasjøen arctic char (Salvelinus alpinus), the multichemical model estimated PBDE biotransformation from higher to lower brominated congeners and improved the correspondence between estimated and measured concentrations in comparison to estimates from the single-chemical food web model. The underestimation of BDE-47, even after considering bioformation due to biotransformation of the otherthree congeners, suggests its formation from additional biotransformation pathways not considered in this application. The model estimates approximate values for congener-specific biotransformation half-lives of 5.7,0.8,1.14, and 0.45 years for BDE-47, -99, -100, and -153, respectively, in large arctic char (S. alpinus) of Lake Ellasjøen.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".