Utility of tissue residues for predicting effects of metals on aquatic organisms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it