Proceedings from the 16th Annual University of Calgary Leaders in Medicine Research Symposium
Bibliographic record
Abstract
The 16th annual Leaders in Medicine (LIM) Research Symposium was held in person at the Health Sciences Centre in Calgary on November 15, 2024. The event featured a keynote address by Dr. Bryan Yipp, titled "Medically Trained Scientists: Fueling Healthcare Revolutions." Dr. Yipp is a clinician-scientist and a graduate of the LIM program. A workshop on "Integrating Anti-Racism, Equity, Diversity, and Inclusion into Health Research and Practise" was presented by Dr. Bukola Salami, Vice President of the Canadian Nurses Association and a board member of the Black Opportunity Fund. Dr. Salami is a Full Professor in the Department of Community Health Sciences at the University of Calgary. Over eighty 80 students presented their research in the poster and oral presentation sessions. Abstracts included represented an incredible diversity of research areas, including biochemistry and molecular biology, biomedical technology and precision health, community health sciences and health services, medical sciences, and neurosciences. The symposium highlighted the incredible remarkable research accomplishments of LIM students and medical students at the University of Calgary.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.017 |
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".