The Integration of Community-Dwelling Non-Human Companion Animals in Research Programs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".