The integration of an individual-based model into toxicokinetics to enhance ecological realism in evaluating population-level impacts of exposure to PCB
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) are classified as one the most extremely regulated anthropogenic contaminants and they have been deeply probed in aquatic ecosystems. However, there is very limited understanding of the population level effects of exposure to PCBs on terrestrial animal species and this has been unanimously indicated as a critical gap in ecological risk assessment. To bridge this information gap, we integrated an individual-based model (IBM) framework into toxicokinetics resulting in a deeper ecological insight to simulate the accumulation of a hypothetical PCB in a terrestrial three-level food chain at the population level. We then validated our simulated system utilizing the observed field bioaccumulation factors in a well-studied terrestrial prey-predator, caribou-wolf. Key findings of the present study indicate that in a PCB-contaminated environment, where all food sources contain some amount of contaminants, producing more offspring results in lower toxic concentration in herbivores (prey) and higher concentration in carnivores (predator). Our novel contribution in this work is that we have achieved a validated system that enables us to investigate toxicokinetics in any animal species involved in a prey-predation interaction by providing lipid, non-lipid, and water fractions in their bodies. Additionally, we demonstrated how using IBM modelling approach could facilitate ecological risk assessment by offering detailed information of generations spanning as many years as required.
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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) | 0.000 | 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 | 0.001 |
| 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".