Recommendations for the development and application of wildlife toxicity reference values
Bibliographic record
Abstract
Toxicity reference values (TRVs) are essential in models used in the prediction of the potential for adverse impacts of environmental contaminants to avian and mammalian wildlife; however, issues in their derivation and application continue to result in inconsistent hazard and risk assessments that present a challenge to site managers and regulatory agencies. Currently, the available science does not support several common practices in TRV derivation and application. Key issues include inappropriate use of hazard quotients and the inability to define the probability of adverse outcomes. Other common problems include the continued use of no-observed- and lowest-observed-adverse-effect levels (NOAELs and LOAELs), the use of allometric scaling for interspecific extrapolation of chronic TRVs, inappropriate extrapolation across classes when data are limited, and extrapolation of chronic TRVs from acute data without scientific basis. Recommendations for future TRV derivation focus on using all available qualified toxicity data to include measures of variation associated with those data. This can be achieved by deriving effective dose (EDx)-based TRVs where x refers to an acceptable (as defined in a problem formulation) reduction in endpoint performance relative to the negative control instead of relying on NOAELs and LOAELs. Recommendations for moving past the use of hazard quotients and dealing with the uncertainty in the TRVs are also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.026 |
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".