Ecological Implications of Steady State and Nonsteady State Bioaccumulation Models
Bibliographic record
Abstract
Accurate predictions on the bioaccumulation of persistent organic pollutants (POPs) are critical for hazard and ecosystem health assessments. Aquatic systems are influenced by multiple stressors including climate change and species invasions and it is important to be able to predict variability in POP concentrations in changing environments. Current steady state bioaccumulation models simplify POP bioaccumulation dynamics, assuming that pollutant uptake and elimination processes become balanced over an organism's lifespan. These models do not consider the complexity of dynamic variables such as temperature and growth rates which are known to have the potential to regulate bioaccumulation in aquatic organisms. We contrast a steady state (SS) bioaccumulation model with a dynamic nonsteady state (NSS) model and a no elimination (NE) model. We demonstrate that both the NSS and the NE models are superior at predicting both average concentrations as well as variation in POPs among individuals. This comparison demonstrates that temporal drivers, such as environmental fluctuations in temperature, growth dynamics, and modified food-web structure strongly determine contaminant concentrations and variability in a changing environment. These results support the recommendation of the future development of more dynamic, nonsteady state bioaccumulation models to predict hazard and risk assessments in the Anthropocene.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".