Common Aquarium Plants as an Enrichment Strategy in Zebrafish Facilities
Bibliographic record
Abstract
An increasing number of laboratories utilize zebrafish as this species is now represented in practically every subfield of biology research. Environmental enrichment has been shown to improve welfare and health of a large number of species of animals studied in laboratories, kept in zoos, or used in agriculture. However, most laboratories keep zebrafish in barren tanks. Artificial and live aquatic plants have been used in a variety of contexts for aquarium fish, and they have also been suggested as a potential enrichment strategy for the zebrafish. However, no systematic studies have been conducted to investigate their beneficial effects in zebrafish husbandry and biology research. In this study, we review some of the potential benefits of using live plants, and argue that systematic analyses for proper choice and use of live plants in zebrafish husbandry are sorely needed. We provide a few examples of aquatic plant species out of the large variety produced for the aquarium hobby that could be adopted to the zebrafish laboratory setting as environmental enrichment. We speculate that once systematic analyses have been conducted, they will show benefits of this ethologically/ecologically relevant enrichment method, one of which will be healthier and less stressed fish leading to increased reproducibility of results in zebrafish research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".