The Influence of Behavioral, Social, and Environmental Factors on Reproducibility and Replicability in Aquatic Animal Models
Bibliographic record
Abstract
The publication of reproducible, replicable, and translatable data in studies utilizing animal models is a scientific, practical, and ethical necessity. This requires careful planning and execution of experiments and accurate reporting of results. Recognition that numerous developmental, environmental, and test-related factors can affect experimental outcomes is essential for a quality study design. Factors commonly considered when designing studies utilizing aquatic animal species include strain, sex, or age of the animal; water quality; temperature; and acoustic and light conditions. However, in the aquatic environment, it is equally important to consider normal species behavior, group dynamics, stocking density, and environmental complexity, including tank design and structural enrichment. Here, we will outline normal species and social behavior of 2 commonly used aquatic species: zebrafish (Danio rerio) and Xenopus (X. laevis and X. tropicalis). We also provide examples as to how these behaviors and the complexity of the tank environment can influence research results and provide general recommendations to assist with improvement of reproducibility and replicability, particularly as it pertains to behavior and environmental complexity, when utilizing these popular aquatic models.
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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 | 0.333 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".