Breeding Zebrafish: A Review of Different Methods and a Discussion on Standardization
Bibliographic record
Abstract
In recent years, a rapidly increasing number of scientific papers have been published that utilize zebrafish (Danio rerio) as an alternative model organism in the study of a wide range of biological phenomena from cancer to behavior. This is, in large part, due to the prolific nature, relative ease of maintenance, and sufficiently high genetic homology of zebrafish to humans. With the surge of zebrafish use in animal research, the variations in methodologies of breeding and husbandry of this species have also increased. Investigators usually focus on the development and implementation of rigorous laboratory control that is specific to their studies. We suggest that the same scrutiny and attention may be required for the methods of breeding and housing of zebrafish. This article reviews a variety of zebrafish husbandry and breeding techniques and conditions employed around the world. It discusses factors ranging from numerous aspects of rearing/housing conditions through the sex ratio of the breeding group to the composition of the diet of zebrafish that may vary across laboratories. It provides some feedback on the potential pros and cons of the different methods. It argues that there is a substantial need for systematic analysis of these methods, that is, the effects of environmental factors on zebrafish health and breeding. It also discusses the question as to whether some degree of standardization of these methods is needed to enhance cross-laboratory comparability of results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".