Bibliographic record
Abstract
son&dquo; (Fig. 1) is perhaps one of the best known paintings in the world, but few people would be able to name the anatomist depicted on the canvas. This was the great Dutch surgeon Nicolaas Tulp. Born in 1593, Tulp studied medicine at the University of Leyden, at that time the foremost school of medicine in Northern Europe. After qualifying he be-came a very successful surgeon in Amsterdam and in 1628 the Mayor and Judges of the City appointed him Praelector in Anatomy at the Surgeon’s Guild. Tulp held the post of anatomy demonstrator for 24 years and carried out his duties with distinction. He was only allowed to dissect male bodies (De Lint, 1933) and the dissec-tions took place in winter time. All the mem-bers of the surgeon’s guild were requested to be present and had to pay a fine if they did not attend. The anatomy lessons can, there-fore, be regarded as a sort of compulsory post-graduate course. Surgeons were, however, not the only persons attending the demonstra-tions. These lessons were, in fact, a social event of some importance and the city magis-trates, important burguers and even ladies, but not children, were invited (Thyssen, 1~29). There is a famous anatomical mistake in Rembrandt’s painting; the flexc>r sublimus digitorum arises from the lateral instead of from the medial side of the elbow (Robin-son, 1919). As has been pointed out by
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.525 | 0.333 |
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".