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Record W4398219376 · doi:10.24908/ohi.v2i1.17187

The Integration of Community-Dwelling Non-Human Companion Animals in Research Programs

2024· article· en· W4398219376 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

VenueOne Health Innovation · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsQueen's University
Fundersnot available
KeywordsCompanion animalCommunicationCognitive sciencePsychology

Abstract

fetched live from OpenAlex

Traditional purpose-bred laboratory animals used in clinical research and testing often do not represent human conditions, with efficacy results often not replicated in human clinical trials. Quality of life among laboratory animals is generally low given exposure to painful procedures, pathologies, and unnatural living conditions. This form of research also contributes to several detrimental environmental effects including pollution, climate change, and altered biodiversity. A One Health solution to this issue involves replacing purpose-bred laboratory animals with companion animals in research given greater similarities to human genetic diversity, healthcare systems, and living environments. The integration of companion animals in this way can also mitigate the effects of research using purpose-bred laboratory animals on animals and the environment. Most published literature on the topic involves veterinary clinical trials evaluating the potential of companion animals as research models for future human applications. We propose a study to explore the perspectives of veterinarians and researchers within this field to better understand some of the strengths and challenges associated with utilizing companion animals in clinical research programs.

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)0.006
metaresearch head score (Gemma)0.000
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 score0.906
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.206
GPT teacher head0.451
Teacher spread0.245 · 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