Survey of methodologies for developing media screening values for ecological risk assessment
Bibliographic record
Abstract
This review evaluates the methodologies of 13 screening value (SV) compilations that have been commonly used in ecological risk assessment (ERA), including compilations from state and U.S. federal agencies, the Oak Ridge National Laboratory (ORNL), Canada, The Netherlands, and Australia. The majority of surfacewater SVs were primarily derived for the protection of aquatic organisms using 2 approaches: (1) a statistical assessment of toxicity values by species groupings, such as "ambient water quality criteria," or (2) extrapolation of a lowest observed adverse effect level determined from limited toxicity data using an uncertainty factor. Sediment SVs were primarily derived for the protection of benthic invertebrates using 2 approaches: (1) statistical interpretations of databases on the incidence of biological effects and chemical concentrations in sediment, or (2) values derived from equilibrium partitioning based on a surfacewater SV. Soil SVs were derived using a diversity of approaches and were usually based on the lowest value determined from soil toxicity to terrestrial plants or invertebrates and, less frequently, from modeled, incidental soil ingestion or chemical accumulation in terrestrial organisms. The various SV compilations and methodologies had varying levels of conservatism and were not consistent in the pathways and receptors considered in the SV derivation. Many SVs were derived from other compilations and were based on outdated values, or they relied on only older toxicity data. Risk assessors involved in ERA should carefully evaluate the technical basis of SVs and consider the uncertainty in any value used to determine the presence or absence of risk and the need for further assessment.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".