Bibliographic record
Abstract
Danielle Dhouailly received her Bachelor of Science degree (Biology) from Paris University. She then worked on a Ph.D. with Philippe Sengel at Grenoble University. After that, she went to Canada and the USA to work with Drs. M. Hardy, R. Sawyer and H. Sun before going back to Grenoble and starting her own laboratory. In the 1970s, she began a series of creative epithelial-mesenchymal recombination experiments among chicken feathers, mouse hairs and lizard scales, and later between rabbit cornea / mouse hairs. Through these original experiments, she elegantly demonstrated that the dermis initiates the formation of cutaneous appendages, while their type is specified by the class and regional origin of the epidermis. Subsequently she showed that the induction of an ectodermal organ, even in an adult epithelium, provokes the appearance of the related tissue stem cells. These works pioneered the concepts which are used in stem cell biology today. Her laboratory now works on the molecular mechanisms underlying these processes. Her papers are typically characterized by an initial insightful observation, followed by rigorous experiments and thoughtful discussions. They are rich with different shades of perspectives, almost like a piece of impressionist art. She loves gardening and her pets. She considers herself a good observer and hard worker driven by curiosity. Her best moments occur when she suddenly becomes enlightened as to an explanation of a basic concept when looking at experimental results or discussing ideas with colleagues. She believes that good results last forever, although interpretations can change. Her advice to young scientists is to be rigorous at the bench, to think hard, and not to be shy to speak up. The following is the story of how this young, female naturalist grew into a well-respected developmental biologist.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".