A review of ecological risk assessment methods for amphibians: Comparative assessment of testing methodologies and available data
Bibliographic record
Abstract
Abstract Historically, ecological risk assessments have rarely included amphibian species, focusing preferentially on other aquatic (fish, invertebrates, algae) and terrestrial wildlife (birds and mammal) species. Often this lack of consideration is due to a paucity of toxicity data, significant variation in study design, uncertainty with regard to exposure, or a combination of all three. Productive risk assessments for amphibians are particularly challenging, given variations in complex life history strategies. Further consideration is needed for the development of useful laboratory animal models and appropriate experimental test procedures that can be effectively applied to the examination of biological response patterns. Using these standardized techniques, risk estimates can be more accurately defined to ensure adequate protection of amphibians from a variety of stress agents. Patterns in toxicity may help to ascertain whether test results from 1 amphibian group (e.g., Urodela) could be sufficiently protective of another (e.g., Anura) and/or whether some nonamphibian aquatic taxonomic groups (e.g., fish or aquatic invertebrates) may be representative of aquatic amphibian life stages. This scope is intended to be a guide in the development of methods that would yield data appropriate for ecological risk decisions applicable to amphibians. Integr Environ Assess Manag 2017;13:601–613. © 2016 SETAC Key Points Amphibians have complex life histories that add levels of complexity in the evaluation of risk at contaminated sites. Life histories for amphibians include exposures to contaminated water, soil, and potentially sediments. Aquatic surrogates (e.g., fish) are only partially useful in predicting effects to a single life stage for some amphibian species. More research is needed to understand the relative importance of exposure to terrestrial life stages in estimating risk.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".