Application of the tissue residue approach in ecological risk assessment
Bibliographic record
Abstract
The objective of this work is to present a critical review of the application of the tissue residue approach (TRA) in ecological risk and/or impact assessment (ERA) of chemical stressors and environmental criteria development. A secondary goal is to develop a framework for integrating the TRA into ecological assessments along with traditional, exposure concentration-based assessment approaches. Although widely recognized for its toxicological appeal, the utility of the TRA in specific applications will depend on numerous factors, such as chemical properties, exposure characteristics, assessment type, availability of tissue residue-response data, and ability to quantify chemical exposure. Therefore, the decision to use the TRA should include an evaluation of the relative strengths, limitations, and uncertainties among exposure and residue-based methods for characterizing toxicological effects. Furthermore, rather than supplanting exposure concentration-based toxicity assessments, the TRA can be highly effective for evaluating and reducing uncertainty when used in a complementary manner (e.g., when evaluating multiple lines of evidence in field studies). To address limitations with the available tissue residue-response data, approaches for extrapolating residue-based toxicity data across species, tissues, and exposure durations are discussed. Some of these approaches rely on predicted residue-response relationships or toxicological models that have an implicit residue-response basis (e.g., biotic ligand model). Because risk to an organism is a function of both its exposure potential and inherent sensitivity (i.e., on a residue basis), bioaccumulation models will be required not only for translating tissue residue criteria into corresponding water and sediment criteria, but also for defining the most vulnerable species in an assemblage (i.e., highly exposed and highly sensitive species). Application of the TRA in ecological assessments and criteria development are summarized for bioaccumulative organic chemicals, TBT, and in situ bioassays using bivalve molluscs.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".