Challenges and opportunities for the environmental risk assessment of chemicals in soils: a recap and follow-up of a SETAC webinar
Bibliographic record
Abstract
Soil sustainability is unquestionable but is under various threats, one of which includes chemical pollution. Under the vision of reaching healthy soils by 2050, the SETAC Webinar "Assessing Risks in Soil: Challenges and Opportunities" was held to understand the current state of soil health in Europe and, identify gaps in the environmental risk assessment (ERA) framework for chemicals entering soils. In reflection of the webinar and soil protection, strategies to describe the current state of soils, including knowledge on existing chemical pollution in soils and soil biodiversity metrics are discussed. With respect to soil pollution by chemicals, the current ERA framework was analysed to identify gaps and needs to protect in-soil biodiversity exposed to chemicals. Here, the importance of soil monitoring and cyclical feedback mechanisms for ERA is highlighted as well as the need to shift the current ERA framework towards a holistic approach that considers long-term impacts on in-soil organisms and soil biodiversity under realistic conditions. Two methods (terrestrial model ecosystems and trait-based approaches) are reviewed as potential suitable tools for the detection of community level effects within the ERA of chemicals entering soils. Finally, the need for cooperation and engagement between member states and stakeholders is tabled.
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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 | 0.035 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".