Evaluation of effects-based methods as monitoring tools for assessing ecological impacts of metals in aquatic ecosystems
Bibliographic record
Abstract
Effects-based methods (EBMs) are considered part of a more integrative strategy for regulating substances of concern under the European Union Water Framework Directive. In general, EBMs have been demonstrated as useful indicators of effects on biota, although links to population and community-level effects are sometimes uncertain. When EBMs are sufficiently specific and sensitive, and links between measured endpoints and apical or higher level effects are established, they can be a useful tool in assessing effects from a specific toxicant or class of toxicants. This is particularly valuable for toxicants that are difficult to measure and for assessing the effects of toxicant mixtures. This paper evaluates 12 EBMs that have been proposed for potential use in the assessment of metals. Each EBM was evaluated with respect to metal specificity and sensitivity, sensitivity to other classes of toxicants, and the strength of the relationship between EBM endpoints and effects observed at the whole organism or population levels of biological organization. The evaluation concluded that none of the EBMs evaluated meet all three criteria of being sensitive to metals, insensitive to other classes of toxicants, and a strong indicator of effects at the whole organism or population level. Given the lack of suitable EBMs for metals, we recommended that the continued development of mixture biotic ligand models (mBLMs) may be the most effective way to achieve the goal of a more holistic approach to regulating metals in aquatic ecosystems. Given the need to further develop and validate mBLMs, we suggest an interim weight-of-evidence approach that includes mBLMs, macroinvertebrate community bioassessment, and measurement of metals in key macroinvertebrate species. This approach provides a near-term solution and simultaneously generates data needed for the refinement and validation of mBLMs. Once validated, it should be possible to rely primarily on mBLMs as an alternative to EBMs for metals. Integr Environ Assess Manag 2023;19:24-31. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.076 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".