Development of a Reduced-Volume Acute Lethality Toxicity Test for <i>Hyalella azteca</i>
Bibliographic record
Abstract
Abstract Effects-directed analysis (EDA) is used to identify the principal toxic components within a complex mixture using iterative steps of chemical fractionation guided by bioassay results. Bioassay selection can be limited in EDA because of the volume requirements for many standardized test methods, and therefore, a reduced-volume acute toxicity test that also provides whole-organism responses is beneficial. To address this need, a static, 7-d, water-only, reduced-volume method (50 mL, 10 organisms) was developed for Hyalella azteca that substantially decreases the volume requirements of standard-volume acute test exposures (200–500 mL of test solution, 15–20 organisms) while maintaining water quality and meeting control survival criteria. Standard- and reduced-volume methods were compared by conducting concurrent toxicity tests with 2 inorganic toxicants (KCl and CdCl2) and 2 organic mixtures of naphthenic acid fraction components (NAFCs) to evaluate test performance. There was no difference between methods when comparing the median lethal concentrations (LC50s) for KCl and both NAFC mixtures (p > 0.05). The LC50s for CdCl2 were statistically different (p = 0.0002); however, this was not considered biologically meaningful because the difference between LC50s was <2-fold. In conclusion, the reduced-volume H. azteca test method generated results comparable to standard-volume test methods and is suitable for use in situations where limited testing material is available, such as when conducting EDA. Environ Toxicol Chem 2020;39:2221–2227. © 2020 Her Majesty the Queen in Right of Canada. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Reproduced with the permission of the Minister of Environment and Climate Change Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".