Sequential Analysis of Lines of Evidence—An Advanced Weight-of-Evidence Approach for Ecological Risk Assessment
Bibliographic record
Abstract
Weight-of-evidence (WOE) approaches have been used in ecological risk assessment (ERA) for many years. The approaches integrate various types of data (e.g., from chemistry, bioassay, and field studies) to make an overall conclusion of risk. However, the current practice of WOE has several important difficulties, including a lack of transparency related to how each line of evidence is weighted or integrated into the overall weight-of-evidence conclusion. Therefore, a sequential analysis of lines of evidence (SALE) approach has been developed that advances the practice of WOE. It was developed for an ERA of chemical stressors but also can be used for nonchemical stressors and is equally applicable to the aquatic and terrestrial environments. The sequential aspect of the SALE process is a significant advancement and is based on 2 primary ideas. First, risks can be ruled out with the use of certain lines of evidence, including modeled hazard quotients (HQs) and comparisons of soil, water, or sediment quality with conservative soil, water or sediment quality guidelines. Thus, the SALE process recognizes that HQs are most useful in ruling out risk rather than predicting risk to ecological populations or communities. Second, the SALE process provides several opportunities to exit the risk assessment process, not only when risks are ruled out, but also when magnitude of effect is acceptable or when little or no evidence exists that associations between stressors and effects may be causal. Thus, the SALE approach explicitly includes interaction between assessors and managers. It illustrates to risk managers how risk management can go beyond the simple derivation of risk-based concentrations of chemicals of concern to risk management goals based on ecological metrics (e.g., species diversity). It also can be used to stimulate discussion of the limitations of the ERA science, and how scientists deal with uncertainty. It should assist risk managers by allowing their decisions to be based on a sequential, flexible, and transparent process that includes direct toxicity risks, indirect risks (via changes in habitat suitability), and the spatial and temporal factors that can influence the risk assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".