Anxiolytic effects of environmental enrichment for mice
Bibliographic record
Abstract
Review question / Objective: The main aim of this review was to critically identify which environmental characteristics consistently improve mice welfare from an affective state perspective.We asked if environmental enrichment versus standard housing would affect anxiety-like behavioural responses in laboratory mice.Condition being studied: Laboratory mice are commonly housed in cages containing bedding materials and, at most, nesting materials and a hiding tube or hut.This type of housing restricts the ability of mice to perform natural behaviours, such as segregation of spaces for elimination and nesting.Compared to mice housed in more complex environments, standard-house mice show a higher incidence of stereotypies, alopecia, and aggression (depending on the type of enrichment).There is abundant evidence that indicate that mice exposed to higher cognitive, sensory and motor stimulation cope better with anxiety-eliciting environments and can recuperate better from chronic stress, pain and stress-induced depression.This evidence indicates that environmental enrichment has a positive effect on affective states in mice.INPLASY registration number: This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) on 10 January 2023 and was last updated on 10 January 2023 (registration number INPLASY202310024).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".