Cell scientist to watch – Serge Mostowy
Bibliographic record
Abstract
ABSTRACT Serge Mostowy earned his bachelor degree in physics and master degree in evolutionary biology from McGill University, Montreal, Canada, where he then continued for his PhD in microbiology and immunology (focusing on Mycobacterium tuberculosis complex genomics) at the McGill Centre for the Study of Host Resistance under the supervision of Marcel Behr. Serge moved to the Institute Pasteur, Paris, France, for post-doctoral work with Pascale Cossart on the cell biology of infection. In 2012, he established his own research group as a Wellcome Trust Research Career Development Fellow in the Department of Medicine at Imperial College and was awarded the Lister Institute of Preventative Medicine Research Prize in 2015. In 2018, Serge was appointed as Professor at the London School of Hygiene & Tropical Medicine. He is the recipient of a Wellcome Trust Senior Research Fellowship and a European Research Council Consolidator Grant. The Mostowy laboratory is working on molecular mechanisms underlying bacterial infection and the role of the cytoskeleton in cellular immunity.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.031 |
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".