The effects of handling on mouse behavior: cupped hands versus familiar or novel huts or tunnels
Bibliographic record
Abstract
Mice are commonly tail-handled, despite evidence that this is aversive. Alternatives include cupping and tunnel handling; both methods are associated with improved welfare outcomes, including reduced anxiety and improved ease of handling, but tail handling may be perceived as more practical for handlers. Practicality may be improved by using handling objects already present in facilities, such as upturned mouse huts. Our first aim was to compare hut handling with the established refined alternatives of cupping and tunnel handling. As both tunnels and huts may be used as part of mouse caging, a second aim was to assess the effects of handling object familiarity (from the home cage vs. a novel object). Outcomes assessed were voluntary interaction with the handler and time spent in the open arms of an elevated plus maze (EPM). Mice (n = 51) were randomly assigned a handling method: cupping, tunnel, or hut. Cages (n = 14) were randomly assigned to contain either a tunnel or hut. Mice underwent 9 days of handling and voluntary interaction tests were conducted on days 1, 5, and 9. On day 10, mice were tested in the EPM. We found that interaction varied with handling object: hut-handled mice spent the most time interacting, followed by tunnel-handled and cupped mice (41.7 ± 1.5 s, 36.1 ± 1.4 s, and 33.0 ± 1.5 s, respectively). Familiar objects improved interaction at the outset, but this difference was no longer evident by day 5. We found no effect of handling object or object familiarity on time spent in the open arms of the EPM. These results suggest that hut handling is a refined handling method; this method may be a practical alternative in facilities that already use huts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".