From scientific evidence to practical solutions
Bibliographic record
Abstract
UCVM), established in 2005, conducts cutting-edge basic, translational, and clinical research across the range of veterinary and comparative biomedical sciences.UCVM is a key player in the university's cross-cutting research theme of One Health and our areas of strength include research in infectious diseases, regenerative and reproductive medicine, cattle health, wildlife health and ecology, pain, and animal welfare.UCVM also shares space with the Cumming School of Medicine.Our close proximity allows sharing state-of-the-art research infrastructure, and close collaboration with other faculties, especially the Faculties of Medicine, Engineering, Science and Kinesiology.This transdisciplinary and multifaculty approach enables cutting-edge interdisciplinary team research and high external funding success.The faculty has 5 prestigious Canada Research Chairs and 5 endowed or industrial chairs.A $44-million 1,000-head working ranch provides a unique living laboratory for real-scale research on production animals at the interface of wildlife and ecosystem health research.Some recent highlights of our research program include the following:
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 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 | 0.232 | 0.491 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".