Equations for modelling contaminant impacts throughout a marine ecosystem
Bibliographic record
Abstract
Biological risk assessment modelling for oil spills using whole-of-ecosystem models has the benefit of assessing species-specific toxicology and the chronic impact of oil spills by layering these impacts on top of the already-built ecosystem within the model. In deterministic models this approach requires tracking contaminants as they move throughout the biology of the ecosystem, from uptake to loss. Here we consolidate, modify, and add to existing equations to produce a synergistic set that can be used to define the impact of contaminants on biological groups throughout the food web. We demonstrate how these equations work, individually as well as in tandem, for oil-based contaminants by implementing them in a three-dimensional marine ecosystem model. We assess the sensitivity of parameters within these equations, showing the impact on the model outcome. Although we focus on oil-based contaminants in our examples, the equations presented can be applied to any contaminants in the aquatic or marine environment. • Whole-of-ecosystem models allow tracking oil spills throughout a marine ecosystem. • Contaminant movements are tracked in the biology of the system from uptake to loss. • We show synergistic equations for contaminants in deterministic ecosystem models. • We exemplify how equations work with parameter estimates for oil-based contaminants. • Presented contaminant equations can be applied to a range of marine contaminants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".