Bibliographic record
Abstract
Since the beginning of my scientific career, I have been interested in understanding the genomic processes involved in the adaptation of populations to various environments, using fungi as models. During both my phD thesis and first postdoc, I looked at the mechanisms involved in cheese adaptation within two cheese ripening species, Penicillium camemberti and P. roqueforti. I was also interested in the evolution of the reproduction mode of P. roqueforti, and in its genetic diversity and population structure. During my second postdoc in Canada, I discovered mating-type genes in the mycorrhizal fungus Rhizophagus irregularis, which was yet considered a typical example of an asexual species. During my third postdoc at the Institut Pasteur, I demonstrated the occurrence of gene flows between populations of the human fungal pathogen Candida albicans. Since 2018 as a CNRS permanent researcher, I have been studying parallel adaptation using several fungi thriving in the cheese environment, combining genome analyses, phenotypic, sociological and historical data, and laboratory experiments. By studying parallel adaptations of phylogenetically distant species to the same ecological niche, my project aims to test if there are convergent adaptations, that is if the same genomic mechanisms and/or the same characters/genes have been the targets of selection. Cheese fungi are prime models to study parallel adaptation because multiple phylogenetically distant species thrive in the same ecological niche, cheese, and this adaptation is the result of strong and recent selection (less than 8000 years ago). My project aims at answering the following questions: 1) do cheese specieshave populations adapted to the cheese environment, genetically differentiated from those present in other environments? 2) how were gene flows reduced? 3) what are the genomic processes involved in adaptive divergence? 4) are these processes the same between species?
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".