Enhancing ecological risk assessment of chemicals for terrestrial ecosystems through ecosystem services approach
Bibliographic record
Abstract
The traditional ecological risk assessment (ERA) approach for chemicals in terrestrial ecosystems focuses on the protection of individual species, populations, or community but often overlooks the aboveground and belowground processes and functions that underpin essential ecosystem services (ES). While ERA aims to protect species diversity, which constitutes or supports the service-providing units driving ES, it does not account for functional redundancy, because not all species contribute equally to ecosystem functions. Although every species is important, especially in specialized ecosystems with a small number of species or low redundancy, focusing on functional groups rather than individual species responses might provide a better measure of how chemicals affect ecosystem functions and services. To address this, grouping species into functional groups such as litter feeders, fungal feeders, saprotrophs, and predators for soil invertebrates, or graminoids and forbs for terrestrial plants, has been suggested to evaluate chemical risks on ES (Fajana et al., 2024). Because ES are driven by functions rather than taxonomy (Kremen, 2005), it is crucial to establish a link between species responses to chemical exposure and ecosystem functions. This connection would enable the assessment of how ES changes in response to chemical impacts on individual species.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".