Review and Development of Best Practices for Toxicity Tests with Dreissenid Mussels
Bibliographic record
Abstract
Since their introduction to North America in the 1980s, research to develop effective control tools for invasive mussels (Dreissena polymorpha and D. rostriformis bugensis) has been ongoing across various research institutions using a range of testing methods. Inconsistencies in experimental methods and reporting present challenges for comparing data, repeating experiments, and applying results. The Invasive Mussel Collaborative established the Toxicity Testing Work Group (TTWG) in 2019 to identify "best practices" and guide development of a standard framework for dreissenid mussel toxicity testing protocols. We reviewed the literature related to laboratory-based dreissenid mussel toxicity tests and determined the degree to which standard guidelines have been used and their applicability to dreissenid mussel testing. We extracted detailed methodology from 99 studies from the peer-reviewed and gray literature and conducted a separate analysis for studies using presettlement and postsettlement mussels. We identified specific components of methods and approaches that could be refined or standardized for dreissenid mussels. These components included species identification, collection methods, size/age class distinction, maintenance practices, testing criteria, sample size, response measures, reporting parameters, exposure methods, and mortality criteria. We consulted experts in the field of aquatic toxicology and dreissenid mussel biology on our proposed. The final recommendations contained in the present review are based on published standard guidelines, methods reported in the published and gray literature, and the expertise of TTWG members and an external panel. In addition, our review identifies research needs for dreissenid mussel testing including improved methods for early-life stage testing, comparative data on life stages and between dreissenid mussel species, inclusion of a reference toxicant, and additional testing of nontarget species (i.e., other aquatic organisms). Environ Toxicol Chem 2023;42:1649-1666. © 2023 His Majesty the King 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. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".