Use of a Food Web Bioaccumulation Model to Uncover Spatially Integrated Polychlorinated Biphenyl Exposures in Detroit River Sport Fish
Bibliographic record
Abstract
We applied and tested a bioenergetic-based, steady-state food web bioaccumulation model to predict polychlorinated biphenyl (PCB) exposures in sport fish of the Detroit River (USA-Canada), which is a Great Lakes area of concern. The PCB concentrations in the sediment and water of the river were found to exhibit high spatial variation. The previously contained areas of high contamination may have spread to adjacent food webs as a result of fish movements. This process may cause biased predictions in single-compartment bioaccumulation models. We performed multiple simulations and contrasted model predictions against a database of 1152 fish sample records comprising 19 sport fish species. The simulations evaluated 4 spatial scales (river-wide, 2-nation, 4-zone, and 6-zone models) to reveal how the spatial heterogeneity of contamination and species-specific movements contribute to variation in fish PCB exposures. The model testing demonstrated that the 2-nation model provided the most accurate global prediction of fish contamination. However, these improvements were not equally observed across all species. The model was subsequently calibrated for poorly performing species, by allowing cross-zone exposures, demonstrating the importance of accounting for specific ecological factors, such as fish movement, to improve PCB bioaccumulation prediction, especially in highly heterogeneous water systems. Environ Toxicol Chem 2019;38:2771-2784. © 2019 SETAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".