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Record W3092473381 · doi:10.1038/s41598-020-73620-0

The epidemiology of fighting in group-housed laboratory mice

2020· article· en· W3092473381 on OpenAlexaff
Jacob H. Theil, Jamie Ahloy‐Dallaire, Elin M. Weber, Brianna N. Gaskill, Kathleen R. Pritchett‐Corning, Stephen A. Felt, Joseph P. Garner

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversité Laval
FundersNational Institutes of Health
KeywordsCageAggressionAnimal welfareEnvironmental healthDemographyMedicineVeterinary medicinePsychologyBiologyEcologyEngineeringPsychiatry

Abstract

fetched live from OpenAlex

Injurious home-cage aggression (fighting) in mice affects both animal welfare and scientific validity. It is arguably the most common potentially preventable morbidity in mouse facilities. Existing literature on mouse aggression almost exclusively examines territorial aggression induced by introducing a stimulus mouse into the home-cage of a singly housed mouse (i.e. the resident/intruder test). However, fighting occurring in mice living together in long-term groups under standard laboratory housing conditions has barely been studied. We performed a point-prevalence epidemiological survey of fighting at a research institution with an approximate 60,000 cage census. A subset of cages was sampled over the course of a year and factors potentially influencing home-cage fighting were recorded. Fighting was almost exclusively seen in group-housed male mice. Approximately 14% of group-housed male cages were observed with fighting animals in brief behavioral observations, but only 14% of those cages with fighting had skin injuries observable from cage-side. Thus simple cage-side checks may be missing the majority of fighting mice. Housing system (the combination of cage ventilation and bedding type), genetic background, time of year, cage location on the rack, and rack orientation in the room were significant risk factors predicting fighting. Of these predictors, only bedding type is easily manipulated to mitigate fighting. Cage ventilation and rack orientation often cannot be changed in modern vivaria, as they are baked in by cookie-cutter architectural approaches to facility design. This study emphasizes the need to invest in assessing the welfare costs of new housing and husbandry systems before implementing them.

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)0.002
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 score0.138
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.091
GPT teacher head0.343
Teacher spread0.252 · 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

Citations43
Published2020
Admission routes1
Has abstractyes

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