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Record W3016858762 · doi:10.1093/ilar/ilaa001

A Good Life for Laboratory Rodents?

2019· review· en· W3016858762 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueILAR Journal · 2019
Typereview
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmRepertoireDistressPsychologySection (typography)Animal welfareAnimal lifeComputer scienceMedicineSocial psychologyEcologyBiologyClinical psychology

Abstract

fetched live from OpenAlex

Most would agree that animals in research should be spared "unnecessary" harm, pain, or distress, and there is also growing interest in providing animals with some form of environmental enrichment. But is this the standard of care that we should aspire to? We argue that we need to work towards a higher standard-specifically, that providing research animals with a "good life" should be a prerequisite for their use. The aims of this paper are to illustrate our vision of a "good life" for laboratory rats and mice and to provide a roadmap for achieving this vision. We recognize that several research procedures are clearly incompatible with a good life but describe here what we consider to be the minimum day-to-day living conditions to be met when using rodents in research. A good life requires that animals can express a rich behavioral repertoire, use their abilities, and fulfill their potential through active engagement with their environment. In the first section, we describe how animals could be housed for these requirements to be fulfilled, from simple modifications to standard housing through to better cage designs and free-ranging options. In the second section, we review the types of interactions with laboratory rodents that are compatible with a good life. In the third section, we address the potential for the animals to have a life outside of research, including the use of pets in clinical trials (the animal-as-patient model) and the adoption of research animals to new homes when they are no longer needed in research. We conclude with a few suggestions for achieving our vision.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.434
GPT teacher head0.503
Teacher spread0.070 · 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