The use of traits-based approaches and eco(toxico)logical models to advance the ecological risk assessment framework for chemicals
Bibliographic record
Abstract
This article presents a framework to diagnose and predict the effects of chemicals, integrating 2 promising tools to incorporate more ecology into ecological risk assessment, namely traits-based approaches and ecological modeling. Traits-based approaches are used increasingly to derive correlations between the occurrence of species traits and chemical exposure from biological and chemical monitoring data. This assessment can also be used in a diagnostic way, i.e., to identify the chemicals probably posing the highest risks to the aquatic ecosystems. The article also describes how ecological models can be used to explore how traits govern the species-substance interactions and to predict effects at the individual, population, and community and ecosystem level, i.e., from the receptor to the landscape level. This can be done by developing models describing the toxicokinetics and toxicodynamics of the chemical in the individual, the life-history of species and the connectivity of populations, determining their recovery, and the food web relations at the community and ecosystem level that determine the indirect effects. Special attention is given on how spatial aspects can be included in the ecological risk assessments using ecological models. The components of the framework are introduced and critically discussed. We describe how the different tools and data generated through experimentation (laboratory and semifield) and biomonitoring can be integrated. The article uses examples from the aquatic compartment, but the concepts that are used, and their integration within the framework, can be generalized to other environmental compartments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".