Utility of tissue residues for predicting effects of metals on aquatic organisms
Bibliographic record
Abstract
As part of a SETAC Pellston Workshop, we evaluated the potential use of metal tissue residues for predicting effects in aquatic organisms. This evaluation included consideration of different conceptual models and then development of several case studies on how tissue residues might be applied for metals, assessing the strengths and weaknesses of these different approaches. We further developed a new conceptual model in which metal tissue concentrations from metal-accumulating organisms (principally invertebrates) that are relatively insensitive to metal toxicity could be used as predictors of effects in metal-sensitive taxa that typically do not accumulate metals to a significant degree. Overall, we conclude that the use of tissue residue assessment for metals other than organometals has not led to the development of a generalized approach as in the case of organic substances. Species-specific and site-specific approaches have been developed for one or more metals (e.g., Ni). The use of gill tissue residues within the biotic ligand model is another successful application. Aquatic organisms contain a diverse array of homeostatic mechanisms that are both metal- and species-specific. As a result, use of whole-body measurements (and often specific organs) for metals does not lead to a defensible position regarding risk to the organism. Rather, we suggest that in the short term, with sufficient validation, species- and site-specific approaches for metals can be developed. In the longer term it may be possible to use metal-accumulating species to predict toxicity to metal-sensitive species with appropriate field validation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".