Enhancing the ecological risk assessment process
Bibliographic record
Abstract
The Ecological Processes and Effects Committee of the US Environmental Protection Agency Science Advisory Board conducted a self-initiated study and convened a public workshop to characterize the state of the ecological risk assessment (ERA), with a view toward advancing the science and application of the process. That survey and analysis of ERA in decision making shows that such assessments have been most effective when clear management goals were included in the problem formulation; translated into information needs; and developed in collaboration with decision makers, assessors, scientists, and stakeholders. This process is best facilitated when risk managers, risk assessors, and stakeholders are engaged in an ongoing dialogue about problem formulation. Identification and acknowledgment of uncertainties that have the potential to profoundly affect the results and outcome of risk assessments also improves assessment effectiveness. Thus we suggest 1) through peer review of ERAs be conducted at the problem formulation stage and 2) the predictive power of risk-based decision making be expanded to reduce uncertainties through analytical and methodological approaches like life cycle analysis. Risk assessment and monitoring programs need better integration to reduce uncertainty and to evaluate risk management decision outcomes. Postdecision audit programs should be initiated to evaluate the environmental outcomes of risk-based decisions. In addition, a process should be developed to demonstrate how monitoring data can be used to reduce uncertainties. Ecological risk assessments should include the effects of chemical and nonchemical stressors at multiple levels of biological organization and spatial scale, and the extent and resolution of the pertinent scales and levels of organization should be explicitly considered during problem formulation. An approach to interpreting lines of evidence and weight of evidence is critically needed for complex assessments, and it would be useful to develop case studies and/or standards of practice for interpreting lines of evidence. In addition, tools for cumulative risk assessment should be developed because contaminants are often released into stressed environments.
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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 | 0.414 | 0.436 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.035 | 0.040 |
| Open science | 0.010 | 0.039 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".