Bibliographic record
Abstract
Félix Vicq d'Azyr was born in 1748 in the small town of Valognes, Normandy. He studied medicine in Paris but he was particularly impressed by the lectures given at the Jardin du Roi by the comparative anatomist Louis Daubenton and the surgeon Antoine Petit. In 1773, Vicq d'Azyr initiated a series of successful lectures on human and animal anatomy at the Paris Medical School, from which he received his medical degree in 1774. He was elected the same year at the Academy of Sciences at age 26, thanks to his outstanding contributions to comparative anatomy. Vicq d'Azyr became widely known after his successful management of a severe cattle plague that occurred in the southern part of France in 1774, an event that led to the foundation of the Royal Society of Medicine in 1778. As Permanent Secretary of this society, Vicq d'Azyr wrote several eulogies that were models of eloquence and erudition and worth him a seat at the French Academy in 1788. Vicq d'Azyr published in 1786 a remarkable anatomy and physiology treatise: a large in-folio that contained original descriptions illustrated by means of nature-sized, colored, human brain figures of a quality and exactitude never attained before. In 1789, Vicq d'Azyr was appointed physician to the Queen Marie-Antoinette and, in 1790, he presented to the Constituent Assembly a decisive plan to reform the teaching of medicine in France. Unfortunately, Vicq d'Azyr did not survive the turmoil of the French Revolution; he died at age 46 on June 20, 1794.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".