Young Investigator Interview with CSCI Distinguished Scientist Awardee Dr. Caroline Quach-Thanh
Bibliographic record
Abstract
Dr. Caroline Quach-Thanh is a Professor in the Departments of Microbiology, Infectious Diseases and Immunology and of Pediatrics at University of Montreal. She is in charge of Infection Prevention and Control at CHU Sainte-Justine where she works as a pediatric infectious diseases specialist and medical microbiologist. Dr. Quach is a clinician-scientist and the Canada Research Chair, Tier 1 in Infection Prevention and Control. In 2022, Dr. Quach-Thanh received the Distinguished Scientist Award 2022 from the Canadian Society for Clinical Investigation. In the same year, she received a Women of Distinction Award-for public service-from the Women's Y Foundation. Dr. Quach-Thanh is the former president from the Association for Medical Microbiology and Infectious Diseases Canada (AMMI), a past Chair of the National Advisory Committee on Immunization (NACI) and is the current chair of the Quebec Immunization Committee. She was named Fellow of the Canadian Academy of Health Sciences and of the Society for Healthcare Epidemiology of America. Dr. QuachThanh was selected as one of the 2019 most Powerful Women in Canada. In 2021, she received the Order of Merit from Université de Montréal and was made Officière de l'Ordre national du Québec in 2022.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".