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Record W2143903070 · doi:10.1258/002367700780384681

Preferences of mice, <i>Mus musculus,</i> for different types of running wheel

2000· article· en· W2143903070 on OpenAlexafffund
S. Banjanin, N. Mrosovsky

Bibliographic record

VenueLaboratory Animals · 2000
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMedical Research Council Canada
KeywordsWheel runningWire meshComputer mouseComputer scienceBiologyMaterials scienceEndocrinologyHuman–computer interaction

Abstract

fetched live from OpenAlex

Mice are increasingly used in research. In particular, their wheel running is often used as a measure of activity, and as a marker of phase of circadian rhythms. Learning about the preferences of mice for different types of wheel may improve their welfare and suggest ways of increasing activity levels. Mice, Mus musculus, were given a choice between different types of running wheel by putting them in cages equipped with two wheels. Strong preferences were shown for wheels with a plastic mesh flooring, rather than the standard metal rods only. The mesh was even preferred over a solid base, although this effect was not seen in mice that had been given access only to wheels with the solid base immediately prior to the choice test. Small diameter wheels, sometimes sold as mouse wheels, were preferred less than standard-sized wheels with rods. The results suggest that types of running wheel often used in laboratories can be improved by considering the animals' preferences. The types of wheel tested here are easy to maintain and entail little additional cost, while increasing the mouse's interest in running and exercise.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.319
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2000
Admission routes2
Has abstractyes

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