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Using Paper Nest Pucks to Prevent Barbering in C57BL/6 Mice

2020· article· en· W3116526477 on OpenAlexaff
Carly M. Moody, Emilie A. Paterson, David Leroux-Petersen, Patricia V. Turner

Bibliographic record

VenueJournal of the American Association for Laboratory Animal Science · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNest (protein structural motif)CageNest boxAgonistic behaviourBiologyAnimal scienceZoologyPsychologyAggressionDevelopmental psychologyBiochemistry

Abstract

fetched live from OpenAlex

Little research has been conducted to examine the influence of various methods of providing nest materials-such as dispersing them, providing them as single units, or clustering them-on the behavior and welfare of group-housed mice. In this study, 6 wk-old C57BL/6NCrl mice were housed 3 per cage and randomized into 1 of 3 nest-material groups: 1) one facial tissue per cage (control; female mice, 3 cages; male mice, 3 cages); 2) an 8-g 'puck' of compressed nesting material and a facial tissue (females, 3 cages; males, 3 cages); or 3) 8 g of dispersed paper strips and a facial tissue (females, 3 cages; males, 3 cages). Mouse behavior (agonistic, stereotypic, nesting), physical examination data, and nest scores were evaluated over 16 d. The results showed that mice in the puck and control groups spent more time manipulating nest materials after cage changes than did mice in the paper-strip group. Average nest scores were highest in the paper-strip group compared with controls and puck cages. Female cages with pucks showed no barbering, whereas all other female mice cages demonstrated barbering. Overall, nest pucks may provide a time-consuming activity for mice and may help protect female C57BL/6 mice from barbering. However, more research is needed to replicate and expand these study results.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.107
GPT teacher head0.399
Teacher spread0.293 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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