Environmental Enrichment Cage for Laboratory Mice: A Downloadable Alternative
Bibliographic record
Abstract
Environmentally enriched housing (EE) provides a stimulating and species-typical environment that enhances brain plasticity and cognition, while reducing disease severity in laboratory animals. However, standardizing EE protocols has been challenging due to issues such as variability, high pricing, or limited laboratory space. To address these challenges, we present a replicable and cost-efficient cage protocol that is accessible to researchers with limited resources and space constraints. The protocol is designed to provide a stimulating and species-typical environment for the animals. It incorporates elements such as social interaction, physical exercise, cognitive stimulation, manipulable objects, environmental variability, and sensory stimulation. As evidenced in our previous studies using our protocol, users can expect to observe similar neuroplastic and health-wise benefits that accompany EE experimental paradigms. These included straightforward step-by-step guide, which allows for construction of functional EE cages in under 8 hr. Basic knowledge of 3D printing and laser cutting is required, but no advanced skills are necessary. The protocol enables researchers to create stimulating and replicable environments that promote animal welfare, enhance brain plasticity, and yield valuable experimental results for low cost. © 2024 Wiley Periodicals LLC. Basic Protocol: An effective and cost-efficient environmental enrichment cage designed to encourage standardization amongst laboratory protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.033 |
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".