Bibliographic record
Abstract
Catherine Martel obtained her PhD from the Université de Montréal and pursued a postdoctoral fellowship first at Mount Sinai School of Medicine in New York (NY, USA), then at Washington University School of Medicine in St Louis (MO, USA), and obtained the Junior Investigator Award for Women from the Arteriosclerosis, Thrombosis and Vascular Biology council of the American Heart Association. Her postdoctoral work is certainly groundbreaking and brings forward new considerations in the field: she discovered that the lymphatic vessel route, the network that runs in parallel with the blood vessels, is critical for removing cholesterol from multiple tissues, including the aortic wall. In 2013, she joined the Arteriosclerosis, Thrombosis and Vascular Biology Early Career Committee, eager to bring a Canadian perspective to the group and get involved in council activities. Since 2014, she is an Assistant Professor at the Department of Medicine at the Université de Montréal, and a research scientist at the Montreal Heart Institute. Her research program now focuses on characterizing the physiopathologic role of the lymphatics in the initiation, progression and regression of atherosclerosis. Basic and translational research will allow her team to identify the causes of lymphatic dysfunction, and eventually target potential therapeutic strategies aiming at improving lymphatic function at the different levels of the atherothrombotic disease. You can follow her laboratory at @LaboMartel_ICM.
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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.042 |
| Insufficient payload (model declined to judge) | 0.005 | 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".