Estimation of Chemical Toxicity to Wildlife Species Using Interspecies Correlation Models
Bibliographic record
Abstract
Ecological risks to wildlife are typically assessed using toxicity data for relatively few species and with limited understanding of differences in species sensitivity to contaminants. Empirical interspecies correlation models were derived from LD50 values for 49 wildlife species and 951 chemicals. The standard wildlife test species Japanese quail (Coturnix japonica) and mallard (Anas platyrhynchos) were determined to be good surrogates for many species within the database. Cross-validation of all models predicted toxicity values within 5-fold and 10-fold of the actual values with 85 and 95% certainty, respectively. Model robustness was not consistently improved by developing correlation models within modes of action (MOA); however, improved models for neurotoxicants, carbamates, and direct acting organophosphorous acetylcholenesterase inhibiting compounds indicate that toxicity estimates may improve if MOA-specific models are built with robust datasets. There was a strong relationship between taxonomic distance and cross-validation prediction success (chi2 = 297, df = 12, p < 0.0001), with uncertainty increasing with larger taxonomic distance between the surrogate and predicted species. Interspecies toxicity correlations provide a tool for estimating contaminant sensitivity with known levels of uncertainty for a diversity of wildlife species.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".