Gérard Orth: From Viral to Human Genes Underlying Warts
Bibliographic record
Abstract
Gérard Orth was born in 1936 and died in 2023.He was picky and prickly.He was sharp and scholarly.He was stern and serious.We loved him, even when he scolded us for forgetting a footnote to an abstract for a communication at a small workshop in a tiny town in the middle of nowhere in the 1960s.Detail was everything to him, but he was also unique in his global vision, which enabled him to make biological and medical breakthroughs in rabbits and humans, but also to discover both viral and host determinants of health and disease.At the time of his death, Gérard Orth was Emeritus Professor at the Institut Pasteur (where he worked from 1979 to 2003) and Emeritus Director of Research at the Centre National de la Recherche Scientifique (CNRS, between 1966 and 2001).He was elected to the French Veterinary Academy in 2003 and the French Academy of Sciences in 2004.Gérard worked at the Institut Gustave Roussy (IGR) in Villejuif from 1961 to 1979, initially in the "Laboratory of Biochemistry and Enzymology" of Claude Paoletti, and then, from 1975 onwards, in his own "Laboratory of Viral Etiologies of Human Cancers".François Gros eventually invited him to join the Institut Pasteur, where he founded and led the "Papillomavirus
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".