The Influence of Behavioral, Social, and Environmental Factors on Reproducibility and Replicability in Aquatic Animal Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it