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Record W4408118170 · doi:10.1038/s41598-025-91687-5

Risk factors for barbering in laboratory mice

2025· article· en· W4408118170 on OpenAlexaff
Anna S. Ratuski, Jacob H. Theil, Jamie Ahloy‐Dallaire, Brianna N. Gaskill, Kathleen R. Pritchett‐Corning, Stephen A. Felt, Joseph P. Garner

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

Barbering is a common abnormal behavior in laboratory mice, where mice pluck their own fur and/or the fur or whiskers of their cage mates. Barbering mice are a concern for welfare and research quality, as well as serving as a spontaneous model of trichotillomania (a hair-pulling disorder in humans). Causes and prevention of barbering are poorly understood, although there is evidence that both biological and environmental factors play a role in its prevalence. Since initial work in this area was done 20 years ago, mouse husbandry has changed dramatically. We provide an updated analysis of risk factors for barbering in laboratory mice based on point prevalence of hair loss in 2544 cages over one year (7007 mice). We analyzed the effects of biological, environmental, and husbandry factors that are known to be stressors for mice. We found that certain risk factors for barbering, such as sex and breeding status, have persisted despite changes in housing. We additionally identified differences in prevalence based on genetic background, housing system, time of year, and a "hotspot" effect showing spatial clustering of barbering. Our findings can be used to increase understanding of this behavior and to inform changes in husbandry to reduce its prevalence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.373
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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