Comparison of tropical and temperate freshwater animal species' acute sensitivities to chemicals: Implications for deriving safe extrapolation factors
Bibliographic record
Abstract
Abstract Toxicity data for tropical species are often lacking for ecological risk assessment. Consequently, tropical and subtropical countries use water quality criteria (WQC) derived from temperate species (e.g., United States, Canada, or Europe) to assess ecological risks in their aquatic systems, leaving an unknown margin of uncertainty. To address this issue, we use species sensitivity distributions of freshwater animal species to determine whether temperate datasets are adequately protective of tropical species assemblages for 18 chemical substances. The results indicate that the relative sensitivities of tropical and temperate species are noticeably different for some of these chemicals. For most metals, temperate species tend to be more sensitive than their tropical counterparts. However, for un-ionized ammonia, phenol, and some pesticides (e.g., chlorpyrifos), tropical species are probably more sensitive. On the basis of the results from objective comparisons of the ratio between temperate and tropical hazardous concentration values for 10% of species, or the 90% protection level, we recommend that an extrapolation factor of 10 should be applied when such surrogate temperate WQCs are used for tropical or subtropical regions and a priori knowledge on the sensitivity of tropical species is very limited or not available.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".