Species extrapolation for the 21st century
Bibliographic record
Abstract
Safety factors are used in ecological risk assessments to extrapolate from the toxic responses of laboratory test species to all species representing that group in the environment. More accurate extrapolation of species responses is important. Advances in understanding the mechanistic basis for toxicological responses and identifying molecular response pathways can provide a basis for extrapolation across species and, in part, an explanation for the variability in whole organism responses to toxicants. We highlight potential short- and medium-term development goals to meet our long-term aspiration of truly predictive in silico extrapolation across wildlife species' response to toxicants. A conceptual approach for considering cross-species extrapolation is presented. Critical information is required to establish evidence-based species extrapolation, including identification of critical molecular pathways and regulatory networks that are linked to the biological mode of action and species' homologies. A case study is presented that examines steroidogenesis inhibition in fish after exposure to fadrozole or prochloraz. Similar effects for each compound among fathead minnow, medaka, and zebrafish were attributed to similar inhibitor pharmacokinetic/pharmacodynamic distributions and sequences of cytochrome P45019A1/2 (CYP19A1/2). Rapid advances in homology modeling allow the prediction of interactions of chemicals with enzymes, for example, CYP19 aromatase, which would eventually allow a prediction of potential aromatase toxicity of new compounds across a range of species. Eventually, predictive models will be developed to extrapolate across species, although substantial research is still required. Knowledge gaps requiring research include defining differences in life histories (e.g., reproductive strategies), understanding tissue-specific gene expression, and defining the role of metabolism on toxic responses and how these collectively affect the power of interspecies extrapolation methods.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".